**Bibliography on Gaussian Process Models in Dynamic
Systems Modelling**

**2018**

Luis Omar Avila, Mariano De Paula, Ernesto Carlos Martinez, Marcelo Luis
Errecalde.

**Robust
insulin estimation under glycemic variability using Bayesian filtering and
Gaussian process models**.

*Biomedical Signal Processing and Control*, Volume 42, Pages 63–72, 2018.

D. Büchler, R. Calandra, B. Schölkopf, J. Peters.

**Control of
Musculoskeletal Systems Using Learned Dynamics Models**.

*IEEE Robotics and Automation Letters*, Volume 3, Pages 3161–3168, 2018.

Tianshi Chen.

**On
kernel design for regularized LTI system identification**.

*Automatica*, Volume 90, Pages 109–122, 2018.

Mohamed Abdelmonim Hassan Darwish, Pepijn Bastiaan Cox, Ioannis Proimadis,
Gianluigi Pillonetto, Roland Tóth.

**Prediction-error
identification of LPV systems: A nonparametric Gaussian regression approach**.

*Automatica*, Volume 97, Pages 92–103, 2018.

Mohamed Abdelmonim Hassan Darwish, Gianluigi Pillonetto, Roland Tóth.

**The
quest for the right kernel in Bayesian impulse response identification: The use
of OBFs**.

*Automatica*, Volume 87, Pages 318–329, 2018.

Niklas Everitt, Giulio Bottegal, Håkan Hjalmarsson.

**An
empirical Bayes approach to identification of modules in dynamic networks**.

*Automatica*, Volume 91, Pages 144–151, 2018.

Y. Fujimoto, I. Maruta, T. Sugie.

**Input Design for
Kernel-Based System Identification From the Viewpoint of Frequency Response**.

*IEEE Transactions on Automatic Control*, Volume 63, Pages 3075–3082,
2018.

Y. Fujimoto, T. Sugie.

**Kernel-Based Impulse
Response Estimation With a Priori Knowledge on the DC Gain**.

*IEEE Control Systems Letters*, Volume 2, Pages 713–718, 2018.

A. Jain, T. Nghiem, M. Morari, R. Mangharam.

**Learning and Control
Using Gaussian Processes**.

In *2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems
(ICCPS)*, Volume , Pages 140–149, 2018.

Sanket Kamthe, Marc Deisenroth.

**Data-Efficient Reinforcement
Learning with Probabilistic Model Predictive Control**.

In *Proceedings of the Twenty-First International Conference on Artificial
Intelligence and Statistics*, Volume 84, Pages 1701–1710, 2018.

Muriel Lang, Martin Kleinsteuber, Sandra Hirche.

**Gaussian process for
6-DoF rigid motions**.

*Autonomous Robots*, Volume 42, Pages 1151–1167, 2018.

Linkai Luo, Yuan Yao, Furong Gao, Chunhui Zhao.

**Mixed-effects
Gaussian process modeling approach with application in injection molding
processes**.

*Journal of Process Control*, Volume 62, Pages 37–43, 2018.

D. W. van der Meer, M. Shepero, A. Svensson, J. Widén, J. Munkhammar.

**Probabilistic
forecasting of electricity consumption, photovoltaic power generation and net
demand of an individual building using Gaussian Processes**.

*Applied Energy*, Volume 213, Pages 195–207, 2018.

R. Quintero Ḿinguez, I. Parra Alonso, D. Fernández-Llorca, M. Á. Sotelo.

**Pedestrian Path, Pose,
and Intention Prediction Through Gaussian Process Dynamical Models and
Pedestrian Activity Recognition**.

*IEEE Transactions on Intelligent Transportation Systems*, Volume , Pages
1–12, 2018.

Biqiang Mu, Tianshi Chen, Lennart Ljung.

**On
asymptotic properties of hyperparameter estimators for kernel-based
regularization methods**.

*Automatica*, Volume 94, Pages 381–395, 2018.

T. Münker, J. Belz, O. Nelles.

**Improved Incorporation
of Prior Knowledge for Regularized FIR Model Identification**.

In *2018 Annual American Control Conference (ACC)*, Volume , Pages
1090–1095, 2018.

Tobias Münker, Timm J. Peter, Oliver Nelles.

**Gray-box identification
with regularized FIR models**.

*Automatisierungstechnik*, Volume 66, Issue 9, Pages 704–713, 2018.

Mehdi Ghasemi Naraghi, Yousef Alipouri.

**Minimum variance lower
bound estimation with Gaussian Process models**.

*Transactions of the Institute of Measurement and Control*, Volume 40,
Pages 1799-1807, 2018.

Gianluigi Pillonetto.

**System
identification using kernel-based regularization: New insights on stability and
consistency issues**.

*Automatica*, Volume 93, Pages 321–332, 2018.

J. Prüher, O. Straka.

**Gaussian Process
Quadrature Moment Transform**.

*IEEE Transactions on Automatic Control*, Volume 63, Pages 2844–2854,
2018.

Maziar Raissi, George Em Karniadakis.

**Hidden
physics models: Machine learning of nonlinear partial differential equations**.

*Journal of Computational Physics*, Volume 357, Pages 125–141, 2018.

M. Raissi, P. Perdikaris, G. Karniadakis.

**Numerical Gaussian Processes
for Time-Dependent and Nonlinear Partial Differential Equations**.

*SIAM Journal on Scientific Computing*, Volume 40, Pages A172-A198, 2018.

Wilmer Ariza Ramirez, Zhi Quan Leong, Hung Nguyen, Shantha Gamini Jayasinghe.

**Non-parametric
dynamic system identification of ships using multi-output Gaussian Processes**.

*Ocean Engineering*, Volume 166, Pages 26–36, 2018.

H. Sheng, J. Xiao, Y. Cheng, Q. Ni, S. Wang.

**Short-Term Solar Power
Forecasting Based on Weighted Gaussian Process Regression**.

*IEEE Transactions on Industrial Electronics*, Volume 65, Pages 300-308,
2018.

Andrew J Smith, Mohammed AlAbsi, Travis Fields.

**Heteroscedastic
Gaussian Process-based System Identification and Predictive Control of a
Quadcopter**.

In *2018 AIAA Atmospheric Flight Mechanics Conference*, 2018.

A. Solin, M. Kok, N. Wahlström, T. B. Schön, S. Särkkä.

**Modeling and
Interpolation of the Ambient Magnetic Field by Gaussian Processes**.

*IEEE Transactions on Robotics*, Volume 34, Pages 1112–1127, 2018.

Yong Tao, Hui Liu, Xianling Deng, Youdong Chen, Hegen Xiong, Zengliang Fang,
Xianwu Xie, Xi Xu.

**A
Robot Self-learning Grasping Control Method Based on Gaussian Process and
Bayesian Algorithm**.

In *Transactions on Intelligent Welding Manufacturing*, Pages 79–98,
Singapore, 2018.

J. Umlauft, L. Pöhler, S. Hirche.

**An Uncertainty-Based
Control Lyapunov Approach for Control-Affine Systems Modeled by Gaussian
Process**.

*IEEE Control Systems Letters*, Volume 2, Pages 483–488, 2018.

J. Vinogradska, B. Bischoff, J. Peters.

**Approximate Value
Iteration Based on Numerical Quadrature**.

*IEEE Robotics and Automation Letters*, Volume 3, Pages 1330–1337, 2018.

Pantelis R Vlachas, Wonmin Byeon, Zhong Y Wan, Themistoklis P Sapsis, Petros
Koumoutsakos.

**Data-driven forecasting
of high-dimensional chaotic systems with long short-term memory networks**.

*Proc. R. Soc. A*, Volume 474, 2018.

J. Wan, S. McLoone.

**Gaussian Process
Regression for Virtual Metrology-Enabled Run-to-Run Control in Semiconductor Manufacturing**.

*IEEE Transactions on Semiconductor Manufacturing*, Volume 31, Pages
12–21, 2018.

K. Worden, W. E. Becker, T. J. Rogers, E. J. Cross.

**On
the confidence bounds of Gaussian process NARX models and their higher-order
frequency response functions**.

*Mechanical Systems and Signal Processing*, Volume 104, Pages 188–223,
2018.

Dongkun Zhang, Liu Yang, George Em Karniadakis.

**Bi-directional
coupling between a PDE-domain and an adjacent Data-domain equipped with
multi-fidelity sensors**.

*Journal of Computational Physics*, Volume 374, Pages 121–134, 2018.

Mattia Zorzi, Alessandro Chiuso.

**The
harmonic analysis of kernel functions**.

*Automatica*, Volume 94, Pages 125–137, 2018.

**2017**

LD Avendaño-Valencia, EN Chatzi, KY Koo, JMW Brownjohn.

**Gaussian
Process Time-Series Models for Structures under Operational Variability. Front**.

*Frontiers in Built Environment*, Volume 3, Pages 69, 2017.

Thomas Beckers, Jonas Umlauft, Sandra Hirche.

**Stable
Model-based Control with Gaussian Process Regression for Robot Manipulators**.

*IFAC-PapersOnLine*, Volume 50, Pages 3877–3884, 2017, 20th IFAC World
Congress.

T. Beckers, J. Umlauft, D. Kulic, S. Hirche.

**Stable Gaussian process
based tracking control of Lagrangian systems**.

In *2017 IEEE 56th Annual Conference on Decision and Control (CDC)*,
Volume , Pages 5180–5185, 2017.

Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, Andreas Krause.

**Safe
Model-based Reinforcement Learning with Stability Guarantees**.

*Advances in Neural Information Processing Systems 30*, 2017.

Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden, Michel Verhaegen.

**System
identification through online sparse Gaussian process regression with input
noise**.

*IFAC Journal of Systems and Control*, Volume 2, Pages 1–11, 2017.

Georgios Birpoutsoukis, Anna Marconato, John Lataire, Johan Schoukens.

**Regularized
nonparametric Volterra kernel estimation**.

*Automatica*, Volume 82, Pages 324–327, 2017.

Giulio Bottegal, Håkan Hjalmarsson, Gianluigi Pillonetto.

**A
new kernel-based approach to system identification with quantized output data**.

*Automatica*, Volume 85, Pages 145–152, 2017.

Yu Cao, Jian Huang, Gangzheng Ding, Yongji Wang.

**Design
of Nonlinear Predictive Control for Pneumatic Muscle Actuator Based on Echo
State Gaussian Process**.

*IFAC-PapersOnLine*, Volume 50, Pages 1952–1957, 2017, 20th IFAC World
Congress.

Gang Cao, Edmund M-K Lai, Fakhrul Alam.

**Gaussian process model predictive
control of unknown non-linear systems**.

*IET Control Theory & Applications*, Volume 11, Pages 703–713, 2017.

Gang Cao, Edmund M.-K. Lai, Fakhrul Alam.

**Gaussian Process Model
Predictive Control of an Unmanned Quadrotor**.

*Journal of Intelligent & Robotic Systems*, Volume 88, Pages 147–162,
2017.

F. P. Carli, T. Chen, L. Ljung.

**Maximum Entropy Kernels for System
Identification**.

*IEEE Transactions on Automatic Control*, Volume 62, Pages 1471–1477,
2017.

Juan Pablo Carbajal, João Paulo Leitão, Carlo Albert, Jörg Rieckermann.

**Appraisal
of data-driven and mechanistic emulators of nonlinear simulators: The case of
hydrodynamic urban drainage models**.

*Environmental Modelling & Software*, Volume 92, Pages 17–27, 2017.

M. Chandorkar, E. Camporeale, S. Wing.

**Probabilistic forecasting
of the disturbance storm time index: An autoregressive Gaussian process
approach**.

*Space Weather*, 2017.

Lester Lik Teck Chan, Junghui Chen.

**Probabilistic
uncertainty based simultaneous process design and control with iterative
expected improvement model**.

*Computers & Chemical Engineering*, Volume 106, Pages 609–620, 2017.

A. H. Chang, C. M. Hubicki, J. J. Aguilar, D. I. Goldman, A. D. Ames, P. A.
Vela.

**Learning to
jump in granular media: Unifying optimal control synthesis with Gaussian
process-based regression**.

In *2017 IEEE International Conference on Robotics and Automation (ICRA)*,
Pages 2154–2160, 2017.

Mohamed A. H. Darwish, John Lataire, Roland Tóth.

**Bayesian
Frequency Domain Identification of LTI Systems with OBFs Kernels**.

*IFAC-PapersOnLine*, Volume 50, Pages 6238–6243, 2017, 20th IFAC World
Congress.

A. Doerr, D. Nguyen-Tuong, A. Marco, S. Schaal, S. Trimpe.

**Model-based policy
search for automatic tuning of multivariate PID controllers**.

In *2017 IEEE International Conference on Robotics and Automation (ICRA)*,
Volume , Pages 5295–5301, 2017.

Stefanos Eleftheriadis, Tom Nicholson, Marc Deisenroth, James Hensman.

**Identification
of Gaussian Process State Space Models**.

*Advances in Neural Information Processing Systems 30*, 2017.

Shimin Feng, Roderick Murray-Smith, Andrew Ramsay.

**Position
stabilisation and lag reduction with Gaussian processes in sensor fusion system
for user performance improvement**.

*International Journal of Machine Learning and Cybernetics*, Volume 8,
Pages 1167–1184, 2017.

A. Golabi, N. Meskin, R. Tóth, J. Mohammadpour.

**A Bayesian Approach
for LPV Model Identification and Its Application to Complex Processes**.

*IEEE Transactions on Control Systems Technology*, Volume 25, Pages
2160–2167, 2017.

R. C. Grande, T. J. Walsh, G. Chowdhary, S. Ferguson, J. P. How.

**Online Regression for
Data With Changepoints Using Gaussian Processes and Reusable Models**.

*IEEE Transactions on Neural Networks and Learning Systems*, Volume 28,
Pages 2115–2128, 2017.

J. Hidalgo-Carrió, D. Hennes, J. Schwendner, F. Kirchner.

**Gaussian
process estimation of odometry errors for localization and mapping**.

In *2017 IEEE International Conference on Robotics and Automation (ICRA)*,
Pages 5696–5701, 2017.

Xiaodan Hong, Biao Huang, Yongsheng Ding, Fan Guo, Lei Chen, Lihong Ren.

**Multi-model
multivariate Gaussian process modelling with correlated noises**.

*Journal of Process Control*, Volume 58, Pages 11–22, 2017.

Yuji Ito, Kenji Fujimoto, Yukihiro Tadokoro, Takayoshi Yoshimura.

**On
Stabilizing Control of Gaussian Processes for Unknown Nonlinear Systems**.

*IFAC-PapersOnLine*, Volume 50, Pages 15385–15390, 2017, 20th IFAC World
Congress.

Nam-Ho Kim, Dawn An, Joo-Ho Choi.

**Prognostics
and Health Management of Engineering Systems**.

Springer, 2017.

Taewan Kim, Wonchul Kim, Seungwon Choi, H. Jin Kim.

**Path
Tracking for a Skid-steer Vehicle using Model Predictive Control with On-line
Sparse Gaussian Process**.

*IFAC-PapersOnLine*, Volume 50, Pages 5755–5760, 2017, 20th IFAC World
Congress.

Taehwan Kim, Jeongho Park, Seongman Heo, Keehoon Sung, Jooyoung Park.

**Characterizing Dynamic
Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian
Process Methods**.

*Sensors*, Volume 17, 2017.

Dimitrios Korkinof, Yiannis Demiris.

**Multi-task
and multi-kernel Gaussian process dynamical systems**.

*Pattern Recognition*, Volume 66, Pages 190–201, 2017.

Andras Kupcsik, Marc Peter Deisenroth, Jan Peters, Ai Poh Loh, Prahlad Vadakkepat,
Gerhard Neumann.

**Model-based
contextual policy search for data-efficient generalization of robot skills**.

*Artificial Intelligence*, Volume 247, Pages 415–439, 2017.

T. V. Le, R. Oentaryo, S. Liu, H. C. Lau.

**Local Gaussian Processes for
Efficient Fine-Grained Traffic Speed Prediction**.

*IEEE Transactions on Big Data*, Volume 3, Pages 194–207, 2017.

Yiqi Liu, Yongping Pan, Daoping Huang, Qilin Wang.

**Fault
prognosis of filamentous sludge bulking using an enhanced multi-output Gaussian
processes regression**.

*Control Engineering Practice*, Volume 62, Pages 46–54, 2017.

César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Guilherme A. Barreto,
Neil D. Lawrence.

**Deep
recurrent Gaussian processes for outlier-robust system identification**.

*Journal of Process Control*, Volume 60, Pages 82–94, 2017, DYCOPS-CAB
2016.

Biqiang Mu, Tianshi Chen, Lennart Ljung.

**Tuning
of Hyperparameters for FIR models – an Asymptotic Theory**.

*IFAC-PapersOnLine*, Volume 50, Pages 2818–2823, 2017, 20th IFAC World
Congress.

Yuta Oka, Yutaka Nakamura, Hiroshi Ishiguro.

**Sampling-Based Motion Planning
with a Prediction Model using Fast Gaussian Process Regression**.

*Electronics and Communications in Japan*, Volume 100, Pages 24–34, 2017.

Yunpeng Pan, Xinyan Yan, Evangelos A. Theodorou, Byron Boots.

**Prediction under
Uncertainty in Sparse Spectrum Gaussian Processes with Applications to
Filtering and Control**.

In *Proceedings of the 34th International Conference on Machine Learning*,
Volume 70, Pages 2760–2768, International Convention Centre, Sydney, Australia,
2017.

Giulia Prando, Alessandro Chiuso, Gianluigi Pillonetto.

**Maximum Entropy vector kernels
for MIMO system identification**.

*Automatica*, Volume 79, Pages 326–339, 2017.

J. Prüher, F. Tronarp, T. Karvonen, S. Särkkä, O. Straka.

**Student-t process
quadratures for filtering of non-linear systems with heavy-tailed noise**.

In *2017 20th International Conference on Information Fusion (Fusion)*,
Volume , Pages 1–8, 2017.

Maziar Raissi, Paris Perdikaris, George Em Karniadakis.

**Machine
learning of linear differential equations using Gaussian processes**.

*Journal of Computational Physics*, Volume 348, Pages 683–693, 2017.

Riccardo S. Risuleo, Giulio Bottegal, Håkan Hjalmarsson.

**Variational
Bayes identification of acyclic dynamic networks**.

*IFAC-PapersOnLine*, Volume 50, Pages 10556–10561, 2017, 20th IFAC World
Congress.

Mark Schillinger, Benjamin Hartmann, Patric Skalecki, Mona Meister, Duy
Nguyen-Tuong, Oliver Nelles.

**Safe
Active Learning and Safe Bayesian Optimization for Tuning a PI-Controller**.

*IFAC-PapersOnLine*, Volume 50, Pages 5967–5972, 2017, 20th IFAC World
Congress.

K. Seo, M. Yamakita.

**Nonlinear time-varying
system identification with recursive Gaussian process**.

In *2017 American Control Conference (ACC)*, Volume , Pages 825–830, 2017.

J. G. Stoddard, J. S. Welsh, H. Hjalmarsson.

**EM-Based Hyperparameter
Optimization for Regularized Volterra Kernel Estimation**.

*IEEE Control Systems Letters*, Volume 1, Pages 388–393, 2017.

Andreas Svensson, Thomas B. Schön.

**A flexible state–space model for learning
nonlinear dynamical systems**.

*Automatica*, Volume 80, Pages 189–199, 2017.

Y. Takaki, K. Fujimoto.

**On output feedback
controller design for Gaussian process state space models**.

In *2017 IEEE 56th Annual Conference on Decision and Control (CDC)*,
Volume , Pages 3652–3657, 2017.

Marc Toussaint.

**A Tutorial on Newton
Methods for Constrained Trajectory Optimization and Relations to SLAM, Gaussian
Process Smoothing, Optimal Control, and Probabilistic Inference**.

Pages 361–392, 2017.

J. Umlauft, T. Beckers, M. Kimmel, S. Hirche.

**Feedback linearization
using Gaussian processes**.

In *2017 IEEE 56th Annual Conference on Decision and Control (CDC)*,
Volume , Pages 5249–5255, 2017.

J. Umlauft, Y. Fanger, S. Hirche.

**Bayesian uncertainty
modeling for programming by demonstration**.

In *2017 IEEE International Conference on Robotics and Automation (ICRA)*,
Volume , Pages 6428–6434, 2017.

Jonas Umlauft, Sandra Hirche.

**Learning Stable
Stochastic Nonlinear Dynamical Systems**.

In *Proceedings of the 34th International Conference on Machine Learning*,
Volume 70, Pages 3502–3510, International Convention Centre, Sydney, Australia,
2017.

J. Umlauft, A. Lederer, S. Hirche.

**Learning stable
Gaussian process state space models**.

In *2017 American Control Conference (ACC)*, Pages 1499–1504, 2017.

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Jan Peters.

**Stability of controllers for Gaussian process dynamics**.

*The Journal of Machine Learning Research*, Volume 18, Pages 3483–3519,
2017.

Yali Wang, Brahim Chaib-draa.

**An
online Bayesian filtering framework for Gaussian process regression: Application
to global surface temperature analysis**.

*Expert Systems with Applications*, Volume 67, Pages 285–295, 2017.

Zhong Yi Wan, Themistoklis P. Sapsis.

**Reduced-space Gaussian Process
Regression for data-driven probabilistic forecast of chaotic dynamical systems**.

*Physica D: Nonlinear Phenomena*, Volume 345, Pages 40–55, 2017.

B. Wehbe, M. Hildebrandt, F. Kirchner.

**Experimental
evaluation of various machine learning regression methods for model
identification of autonomous underwater vehicles**.

In *2017 IEEE International Conference on Robotics and Automation (ICRA)*,
Volume , Pages 4885–4890, 2017.

K. Worden, T. Rogers, E. J. Cross.

**Identification of
Nonlinear Wave Forces Using Gaussian Process NARX Models**.

In *Nonlinear Dynamics, Volume 1*, Pages 203–221, Cham, 2017.

Keith Worden, Cecilia Surace, William Becker.

**Uncertainty
Bounds on Higher-Order FRFs from Gaussian Process NARX Models**.

*Procedia Engineering*, Volume 199, Pages 1994–2000, 2017, X International
Conference on Structural Dynamics, EURODYN 2017.

M. Xiloyannis, C. Gavriel, A. A. C. Thomik, A. A. Faisal.

**Gaussian Process
Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control
With Natural Hand Kinematics**.

*IEEE Transactions on Neural Systems and Rehabilitation Engineering*,
Volume 25, Pages 1785–1801, 2017.

X. Yuan, Z. Ge, B. Huang, Z. Song.

**A Probabilistic
Just-in-Time Learning Framework for Soft Sensor Development With Missing Data**.

*IEEE Transactions on Control Systems Technology*, Volume 25, Pages
1124–1132, 2017.

Mahdi Zarghami, S. Hassan Hosseinnia, Mehrdad Babazadeh.

**Optimal
Control of EGR System in Gasoline Engine Based on Gaussian Process**.

*IFAC-PapersOnLine*, Volume 50, Pages 3750–3755, 2017, 20th IFAC World
Congress.

Yulai Zhang, Guiming Luo.

**Recursive prediction
algorithm for non-stationary Gaussian Process**.

*Journal of Systems and Software*, Volume 127, Pages 295–301, 2017.

Mattia Zorzi, Alessandro Chiuso.

**Sparse plus low rank network
identification: A nonparametric approach**.

*Automatica*, Volume 76, Pages 355–366, 2017.

**2016**

T. Beckers, S. Hirche.

**Stability of
Gaussian Process State Space Models**.

In *Proceedings of the European Control Conference (ECC)*, 2016.

T. Beckers, S. Hirche.

**Equilibrium
distributions and stability analysis of Gaussian Process State Space Models**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
6355–6361, 2016.

F. Berkenkamp, R. Moriconi, A. P. Schoellig, A. Krause.

**Safe learning of regions of
attraction for uncertain, nonlinear systems with Gaussian processes**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
4661–4666, 2016.

Felix Berkenkamp, Angela P Schoellig, Andreas Krause.

**Safe Controller Optimization for
Quadrotors with Gaussian Processes**.

In *Proceedings of the IEEE International Conference on Robotics and
Automation (ICRA)*, 2016.

Giulio Bottegal, Aleksandr Y. Aravkin, Håkan Hjalmarsson, Gianluigi Pillonetto.

**Robust
EM kernel-based methods for linear system identification**.

*Automatica*, Volume 67, Pages 114–126, 2016.

G. Cao, E. M-K Lai, F. Alam.

**Gaussian
Process based Model Predictive Control for Linear Time Varying systems**.

In *2016 IEEE 14th International Workshop on Advanced Motion Control (AMC)*,
Pages 251–256, 2016.

A. Carron, M. Todescato, R. Carli, L. Schenato, G. Pillonetto.

**Machine learning
meets Kalman Filtering**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
4594–4599, 2016.

Tanmoy Chatterjee, Souvik Chakraborty, Rajib Chowdhury.

**A
bi-level approximation tool for the computation of FRFs in stochastic dynamic
systems**.

*Mechanical Systems and Signal Processing*, Volume 70-71, Pages 484 - 505,
2016.

Lester Lik Teck Chan, Tao Chen, Junghui Chen.

**PID
based nonlinear processes control model uncertainty improvement by using
Gaussian process model**.

*Journal of Process Control*, Volume 42, Pages 77–89, 2016.

Tianshi Chen, Tohid Ardeshiri, Francesca P Carli, Alessandro Chiuso, Lennart
Ljung, Gianluigi Pillonetto.

**Maximum entropy properties of
discrete-time first-order stable spline kernel**.

*Automatica*, Volume 66, Pages 34–38, 2016.

T. Chen, G. Pillonetto, A. Chiuso, L. Ljung.

**Continuous-time DC
kernel — A stable generalized first order spline kernel**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
4647–4652, 2016.

A Chiuso.

**Regularization and Bayesian
learning in dynamical systems: Past, present and future**.

*Annual Reviews in Control*, Volume 41, Pages 24–38, 2016.

Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence.

**Variational Inference
for Latent Variables and Uncertain Inputs in Gaussian Processes**.

*Journal of Machine Learning Research*, Volume 17, Pages 1–62, 2016.

M. A. H. Darwish, Roland Tóth.

**An on-line
compensation of input additive disturbances : an evolving Gaussian process
models approach**.

*25th European Research Network System Identification (ERNSI) Workshop*,
2016, Research poster.

Y. Fanger, J. Umlauft, S. Hirche.

**Gaussian processes for
dynamic movement primitives with application in knowledge-based cooperation**.

In *2016 IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS)*, Volume , Pages 3913–3919, 2016.

K. Fujimoto, Y. Takaki.

**On system
identification for ARMAX models based on the variational Bayesian method**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
1217–1222, 2016.

PL Green.

**Towards
the Diagnosis and Simulation of Discrepancies in Dynamical Models**.

*Model Validation and Uncertainty Quantification, Volume 3*, 2016.

J. Han, X. P. Zhang, F. Wang.

**Gaussian
Process Regression Stochastic Volatility Model for Financial Time Series**.

*IEEE Journal of Selected Topics in Signal Processing*, Volume 10, Pages
1015–1028, 2016.

Toni Karvonen, Simo Särkkä.

**Approximate
state-space Gaussian processes via spectral transformation**.

In *Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th
International Workshop on*, Pages 1–6, 2016.

Edgar D. Klenske, Philipp Hennig.

**Dual
Control for Approximate Bayesian Reinforcement Learning**.

*Journal of Machine Learning Research*, Volume 17, Pages 1–30, 2016.

E.D. Klenske, M.N. Zeilinger, B. Scholkopf, P. Hennig.

**Gaussian
Process-Based Predictive Control for Periodic Error Correction**.

*Control Systems Technology, IEEE Transactions on*, Volume 24, Pages
110-121, 2016.

Juš Kocijan.

**Modelling and
Control of Dynamic Systems Using Gaussian Process Models**.

Springer International Publishing, 2016.

Juš Kocijan, Dejan Petelin.

**Closed-Loop
Control with Evolving Gaussian Process Models**.

*Complex Systems*, 2016.

John Lataire, Tianshi Chen.

**Transfer
function and transient estimation by Gaussian process regression in the
frequency domain**.

*Automatica*, Volume 72, Pages 217–229, 2016.

Yiqi Liu, Hongjun Xiao, Yongping Pan, Daoping Huang, Qilin Wang.

**Development
of multiple-step soft-sensors using a Gaussian process model with application
for fault prognosis**.

*Chemometrics and Intelligent Laboratory Systems*, Volume 157, Pages
85–95, 2016.

Anna Marconato, Maarten Schoukens, Johan Schoukens.

**Filter-based regularisation for
impulse response modelling**.

*IET Control Theory & Applications*, Volume 11, Pages 194–204, 2016.

César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou, Jeremy Forth, Guilherme
A Barreto, Neil D Lawrence.

**Recurrent Gaussian Processes**.

In *International Conference on Learning Representations (ICLR)*, 2016.

César Lincoln C. Mattos, Andreas Damianou, Guilherme A Barreto, Neil Lawrence.

**Latent
Autoregressive Gaussian Process Models for Robust System Identification**.

In *11th IFAC Symposium on Dynamics and Control of Process System (DYCOPS)*,
2016.

J. R. Medina, S. Endo, S. Hirche.

**Impedance-based
Gaussian Processes for predicting human behavior during physical interaction**.

In *2016 IEEE International Conference on Robotics and Automation (ICRA)*,
Pages 3055–3061, 2016.

F. Meier, S. Schaal.

**Drifting
Gaussian processes with varying neighborhood sizes for online model learning**.

In *2016 IEEE International Conference on Robotics and Automation (ICRA)*,
Pages 264–269, 2016.

Rajesh Kumar Neerukatti, Masoud Yekani Fard, Aditi Chattopadhyay.

**Gaussian
Process-Based Particle-Filtering Approach for Real-Time Damage Prediction with
Application**.

*Journal of Aerospace Engineering*, Volume 30, Pages 04016080, 2016.

Shayegan Omidshafiei, Ali-Akbar Agha-Mohammadi, Yu Fan Chen, Nazim Kemal Ure,
Shih-Yuan Liu, Brett T Lopez, Rajeev Surati, Jonathan P How, John Vian.

**Measurable Augmented
Reality for Prototyping Cyberphysical Systems: A Robotics Platform to Aid the
Hardware Prototyping and Performance Testing of Algorithms**.

*IEEE Control Systems*, Volume 36, Pages 65–87, 2016.

P. Wen, J. Wang, J. Zhou, H. Wu, Q. Jin.

**Gaussian
process based online dynamic modeling of neuromuscular blockade**.

In *Chinese Control and Decision Conference (CCDC)*, Pages 6982–6987,
2016.

Gianluigi Pillonetto.

**A
new kernel-based approach to hybrid system identification**.

*Automatica*, Volume 70, Pages 21–31, 2016.

Gianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao,
Lennart Ljung.

**Regularized linear system
identification using atomic, nuclear and kernel-based norms: the role of the
stability constraint**.

*Automatica*, Volume 69, Pages 137–149, 2016.

G. Prando, D. Romeres, A. Chiuso.

**Online identification of
time-varying systems: A Bayesian approach**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
3775–3780, 2016.

G. Prando, D. Romeres, G. Pillonetto, A. Chiuso.

**Classical vs. Bayesian methods
for linear system identification: Point estimators and confidence sets**.

In *2016 European Control Conference (ECC)*, Pages 1365–1370, 2016.

Rishik Ranjan, Biao Huang, Alireza Fatehi.

**Robust Gaussian
process modeling using EM algorithm**.

*Journal of Process Control*, Volume 42, Pages 125–136, 2016.

R. S. Risuleo, G. Bottegal, H. Hjalmarsson.

**Kernel-based system identification
from noisy and incomplete input-output data**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages 2061–2066,
2016.

F. H. M. D. Rocha, V. Grassi, V. C. Guizilini, F. Ramos.

**Model
Predictive Control of a Heavy-Duty Truck Based on Gaussian Process**.

In *2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics
Symposium (LARS/SBR)*, Pages 97–102, 2016.

D. Romeres, G. Prando, G. Pillonetto, A. Chiuso.

**On-line Bayesian system
identification**.

In *2016 European Control Conference (ECC)*, Pages 1359–1364, 2016.

D. Romeres, M. Zorzi, R. Camoriano, A. Chiuso.

**Online semi-parametric learning
for inverse dynamics modeling**.

In *2016 IEEE 55th Conference on Decision and Control (CDC)*, Pages
2945–2950, 2016.

Jens Schreiter, Duy Nguyen-Tuong, Marc Toussaint.

**Efficient
sparsification for Gaussian process regression**.

*Neurocomputing*, Volume 192, Pages 29–37, 2016.

Andreas Svensson, Arno Solin, Simo Särkkä, Thomas B Schön.

**Computationally efficient
Bayesian learning of Gaussian process state space models**.

In *Proceedings of the 19th International Conference on Artificial
Intelligence and Statistics (AISTATS)*, Pages 213–221, 2016.

Julia Vinogradska, Bastian Bischoff, Duy Nguyen-Tuong, Henner Schmidt, Anne
Romer, Jan Peters.

**Stability
of Controllers for Gaussian Process Forward Models**.

In *Proceedings of The 33rd International Conference on Machine Learning*,
Pages 545–554, 2016.

Hongchuan Wei, Wenjie Lu, Pingping Zhu, Silvia Ferrari, Miao Liu, Robert H.
Klein, Shayegan Omidshafiei, Jonathan P. How.

**Information
value in nonparametric Dirichlet-process Gaussian-process (DPGP) mixture models**.

*Automatica*, Volume 74, Pages 360–368, 2016.

Weili Xiong, Wei Zhang, Baoguo Xu, Biao Huang.

**JITL
based MWGPR soft sensor for multi-mode process with dual-updating strategy**.

*Computers & Chemical Engineering*, Volume 90, Pages 260–267, 2016.

J. Yan, K. Li, E. W. Bai, J. Deng, A. M. Foley.

**Hybrid
Probabilistic Wind Power Forecasting Using Temporally Local Gaussian Process**.

*IEEE Transactions on Sustainable Energy*, Volume 7, Pages 87–95, 2016.

J. Zhao, S. Sun.

**High-Order
Gaussian Process Dynamical Models for Traffic Flow Prediction**.

*IEEE Transactions on Intelligent Transportation Systems*, Volume 17,
Pages 2014–2019, 2016.

Jing Zhao, Shiliang Sun.

**Variational
Dependent Multi-output Gaussian Process Dynamical Systems**.

*Journal of Machine Learning Research*, Volume 17, Pages 1–36, 2016.

Chi Zhang, Haikun Wei, Xin Zhao, Tianhong Liu, Kanjian Zhang.

**A Gaussian
process regression based hybrid approach for short-term wind speed prediction**.

*Energy Conversion and Management*, Volume 126, Pages 1084–1092, 2016.

**2015**

F. Abbasi, J. Mohammadpour, R. Tóth, N. Meskin.

**A Bayesian approach for
model identification of LPV systems with uncertain scheduling variables**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Volume ,
Pages 789–794, 2015.

Ki-Uhn Ahn, Deuk-Woo Kim, Young-Jin Kim, Cheol-Soo Park, In-Han Kim.

**Gaussian
Process Model for Control of an Existing Building**.

*Energy Procedia*, Volume 78, Pages 2136 – 2141, 2015.

L.O. Avila, Ernesto C. Martinez.

**A Grid-Based Tool
for Optimal Performance Monitoring of an Artificial Pancreas**.

*VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná,
Argentina 29, 30 & 31 October 2014*, *IFMBE Proceedings*, 2015.

A.M. Axelrod, H.A. Kingravi, G.V. Chowdhary.

**Gaussian process based
subsumption of a parasitic control component**.

In *American Control Conference (ACC), 2015*, Pages 2888–2893, 2015.

F. Berkenkamp, A.P. Schoellig.

**Safe
and robust learning control with Gaussian processes**.

In *Control Conference (ECC), 2015 European*, Pages 2496-2501, 2015.

Hildo Bijl, Jan-Willem van Wingerden, Thomas B. Schoen, Michel Verhaegen.

**Online
sparse Gaussian process regression using FITC and PITC approximations**.

*IFAC-PapersOnLine*, Volume 48, Issue 28, Pages 703–708, 2015.

Silvia Bonettini, Alessandro Chiuso, Marco Prato.

**A scaled gradient projection
method for Bayesian learning in dynamical systems**.

*SIAM Journal on Scientific Computing*, Volume 37, Pages A1297–A1318,
2015.

Giulio Bottegal, Gianluigi Pillonetto, Håkan Hjalmarsson.

**Bayesian kernel-based system
identification with quantized output data**.

*IFAC-PapersOnLine*, Volume 48, Pages 455–460, 2015.

Giulio Bottegal, Riccardo S. Risuleo, Håkan Hjalmarsson.

**Blind system identification using
kernel-based methods**.

*IFAC-PapersOnLine*, Volume 48, Pages 466–471, 2015.

S.P. Chatzis, D. Kosmopoulos.

**A
Latent Manifold Markovian Dynamics Gaussian Process**.

*Neural Networks and Learning Systems, IEEE Transactions on*, Volume 26,
Pages 70–83, 2015.

Tianshi Chen, Lennart Ljung.

**On
kernel structures for regularized system identification (II): a system theory
perspective**.

*IFAC-PapersOnLine*, Volume 48, Pages 1041–1046, 2015, 17th IFAC Symposium
on System Identification SYSID 2015.

Tianshi Chen, Gianluigi Pillonetto, Alessandro Chiuso, Lennart Ljung.

**Spectral analysis of
the DC kernel for regularized system identification**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Pages
4017–4022, 2015.

Xin Chen, Penghuan Xie, Yong He, Min Wu.

**Coordinated
learning based on time-sharing tracking framework and Gaussian regression for
continuous multi-agent systems**.

*Engineering Applications of Artificial Intelligence*, Volume 41, Pages
56–64, 2015.

G. Chowdhary, H.A. Kingravi, J.P. How, P.A. Vela.

**Bayesian
Nonparametric Adaptive Control Using Gaussian Processes**.

*Neural Networks and Learning Systems, IEEE Transactions on*, Volume 26,
Pages 537–550, 2015.

Andreas Damianou, Neil D. Lawrence.

**Semi-described
and semi-supervised learning with Gaussian processes**.

*31st Conference on Uncertainty in Artificial Intelligence*, 2015.

M. Darwish, P. Cox, G. Pillonetto, R. Tóth.

**Bayesian identification
of LPV Box-Jenkins models**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Volume ,
Pages 66–71, 2015.

Mohamed Darwish, Gianluigi Pillonetto, Roland Toth.

**Perspectives of
Orthonormal Basis Functions Based Kernels in Bayesian System Identification**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Pages
2713–2718, 2015.

M.P. Deisenroth, D. Fox, C.E. Rasmussen.

**Gaussian
Processes for Data-Efficient Learning in Robotics and Control**.

*Pattern Analysis and Machine Intelligence, IEEE Transactions on*, Volume
37, Pages 408–423, 2015.

Pradipto Ghosh, Bruce A. Conway.

**Spatial
statistical point prediction guidance for heating-rate-limited aeroassisted
orbital transfer**.

*Acta Astronautica*, Volume 111, Pages 257 – 269, 2015.

P. Guo, X. Wang.

**Vibration
monitoring by Gaussian process regression for wind turbine towers**.

*Dongli Gongcheng Xuebao/Journal of Chinese Society of Power Engineering*,
Volume 35, Pages 380–386, 2015.

Tomohiro Hachino, Hitoshi Takata, Seiji Fukushima, Yasutaka Igarashi.

**Model
Predictive Control of Electric Power Systems Based on Gaussian Process
Predictors**.

*Journal of Automation and Control Engineering*, Volume 3, Pages 418–424,
2015.

Hyuk Kang, F. C. Park.

**Motion optimization
using Gaussian process dynamical models**.

*Multibody System Dynamics*, Volume 34, Pages 307–325, 2015.

K. Kronander, M. Khansari, A. Billard.

**Incremental
motion learning with locally modulated dynamical systems**.

*Robotics and Autonomous Systems*, Volume 70, Pages 52 – 62, 2015.

Zitao Liu, Milos Hauskrecht.

**Clinical
time series prediction: Toward a hierarchical dynamical system framework**.

*Artificial Intelligence in Medicine*, Volume 65, Pages 5 - 18, 2015,
Artificial Intelligence in Medicine AIME 2013.

César Lincoln C. Mattos, José Daniel A. Santos, Guilherme A. Barreto.

**An Empirical
Evaluation of Robust Gaussian Process Models for System Identification**.

Pages 172–180, 2015.

B. Michini, T.J. Walsh, A.-A. Agha-Mohammadi, J.P. How.

**Bayesian
Nonparametric Reward Learning From Demonstration**.

*Robotics, IEEE Transactions on*, Volume 31, Pages 369–386, 2015.

J. Ngeo, T. Tamei, K. Ikeda, T. Shibata.

**Modeling dynamic
high-DOF finger postures from surface EMG using nonlinear synergies in latent
space representation**.

In *Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual
International Conference of the IEEE*, Pages 2095-2098, 2015.

Yuya Okadome, Yutaka Nakamura, Hiroshi Ishiguro.

**Sampling-based Motion
Planning with a Prediction Model using Fast Gaussian Process Regression**.

*IEEJ Transactions on Electronics, Information and Systems*, Volume 135,
Pages 526–533, 2015, (in Japanese).

Chris J. Ostafew, Angela P. Schoellig, Timothy D. Barfoot.

**Conservative
to confident: treating uncertainty robustly within learning-based control**.

In *Proc. of the IEEE International Conference on Robotics and Automation
(ICRA)*, Pages 421–427, 2015.

Y. Pan, E. A. Theodorou.

**Data-driven
differential dynamic programming using Gaussian processes**.

In *2015 American Control Conference (ACC)*, Pages 4467-4472, 2015.

Gianluigi Pillonetto.

**Identification of
hybrid systems using stable spline kernels**.

In *2015 IEEE 25th International Workshop on Machine Learning for Signal
Processing (MLSP)*, Pages 1–6, 2015.

Gianluigi Pillonetto, Alessandro Chiuso.

**Tuning
complexity in regularized kernel-based regression and linear system identification:
The robustness of the marginal likelihood estimator**.

*Automatica*, Volume 58, Pages 106 – 117, 2015.

I. Proimadis, H. J. Bijl, J. W. van Wingerden.

**A
kernel based approach for LPV subspace identification**.

*IFAC-PapersOnLine*, Volume 48, Pages 97–102, 2015.

Jakub Prüher, Ladislav Král.

**Functional
Dual Adaptive Control with Recursive Gaussian Process Model**.

In *Journal of Physics: Conference Series*, Volume 659, 2015.

R. Quintero, I. Parra, D.F. Llorca, M.A. Sotelo.

**Pedestrian Intention and
Pose Prediction through Dynamical Models and Behaviour Classification**.

In *Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International
Conference on*, Pages 83-88, 2015.

Riccardo S. Risuleo, Giulio Bottegal, Håkan Hjalmarsson.

**A kernel-based approach to
Hammerstein system identication**.

*IFAC-PapersOnLine*, Volume 48, Pages 1011–1016, 2015.

R. S. Risuleo, G. Bottegal, H. Hjalmarsson.

**On the estimation of initial
conditions in kernel-based system identification**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Pages
1120–1125, 2015.

R. S. Risuleo, G. Bottegal, H. Hjalmarsson.

**A new kernel-based approach to
overparameterized Hammerstein system identification**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Pages
115–120, 2015.

D. Romeres, G. Pillonetto, A. Chiuso.

**Identification of stable models
via nonparametric prediction error methods**.

In *2015 European Control Conference (ECC)*, Pages 2044–2049, 2015.

J. Schreiter, P. Englert, D. Nguyen-Tuong, M. Toussaint.

**Sparse
Gaussian Process Regression for Compliant, Real-Time Robot Control**.

In *Proceedings of 2014 IEEE International Conference on Robotics and
Automation (ICRA)*, 2015.

Ahmed Shokry, Francesca Audino, Patricia Vicente, Gerard Escudero, Montserrat
Perez Moya, Moises Graells, Antonio Espuña.

**Modeling
and Simulation of Complex Nonlinear Dynamic Processes Using Data Based Models:
Application to Photo-Fenton Process**.

*12th International Symposium on Process Systems Engineering and 25th
European Symposium on Computer Aided Process Engineering*, *Computer Aided
Chemical Engineering*, 2015.

H. Soh, Y. Demiris.

**Spatio-Temporal
Learning With the Online Finite and Infinite Echo-State Gaussian Processes**.

*Neural Networks and Learning Systems, IEEE Transactions on*, Volume 26,
Pages 522–536, 2015.

Martin Stepančič, Alexandra Grancharova, Juš Kocijan.

**Adaptive
MPC based on probabilistic black-box input-output model**.

*Comptes Rendus de l Academie Bulgare des Sciences*, Volume 68, Pages 767–774,
2015.

Pei Sun, Junghui Chen, Lei Xie.

**Self-active
and recursively selective Gaussian process models for nonlinear distributed
parameter systems**.

*Chemical Engineering Science*, Volume 123, Pages 125 – 136, 2015.

S. Urban, M. Ludersdorfer, P. van der Smagt.

**Sensor
Calibration and Hysteresis Compensation With Heteroscedastic Gaussian Processes**.

*IEEE Sensors Journal*, Volume 15, Pages 6498–6506, 2015.

Michail D. Vrettas, Manfred Opper, Dan Cornford.

**Variational
mean-field algorithm for efficient inference in large systems of stochastic
differential equations**.

*Phys. Rev. E*, Volume 91, Issue 1, Pages 012148, 2015.

Tianfang Xu, Albert J. Valocchi.

**A
Bayesian approach to improved calibration and prediction of groundwater models
with structural error**.

*Water Resources Research*, Volume 51, Pages 9290–9311, 2015.

Xiaoke Yang, J.M. Maciejowski.

**Fault
tolerant control using Gaussian processes and model predictive control**.

*International Journal of Applied Mathematics and Computer Science*,
Volume 25, Issue 1, Pages 133–148, 2015.

Xiaoke Yang, Jan Maciejowski.

**Risk-Sensitive
Model Predictive Control with Gaussian Process Models**.

*IFAC-PapersOnLine*, Volume 48, Pages 374–379, 2015, 17th IFAC Symposium
on System Identification SYSID 2015.

Fu Yongfeng, Xu Ouguan, Chen Weijie, Ji Haifeng.

**Adaptive soft sensor
modeling method based on multi-model dynamic fusion and its industrial
application**.

In *Instrumentation and Measurement Technology Conference (I2MTC), 2015 IEEE
International*, Pages 1308-1313, 2015.

Le Zhou, Junghui Chen, Zhihuan Song.

**Recursive
Gaussian Process Regression Model for Adaptive Quality Monitoring in Batch
Processes**.

*Mathematical Problems in Engineering*, Volume 2015, Pages 761280, 2015.

M. Zorzi, A. Chiuso.

**A Bayesian approach to sparse plus
low rank network identification**.

In *2015 54th IEEE Conference on Decision and Control (CDC)*, Pages
7386-7391, 2015.

**2014**

Ali Abusnina, Daniel Kudenko, Rolf Roth.

**Selection of Covariance
Functions in Gaussian Process-based Soft Sensors**.

In *Industrial Technology (ICIT), 2014 IEEE International Conference on*,
2014.

Ali Abusnina, Daniel Kudenko, Rolf Roth.

**Gaussian Process-Based
Inferential Control System**.

*International Joint Conference SOCO'14-CISIS'14-ICEUTE'14*, *Advances
in Intelligent Systems and Computing*, 2014.

N. T. Alberto, M. Mistry, F. Stulp.

**Computed torque
control with variable gains through Gaussian process regression**.

In *2014 IEEE-RAS International Conference on Humanoid Robots*, Pages
212–217, 2014.

Adriana Amicarelli, Olga Quintero, Fernando di Sciascio.

**Behavior comparison for biomass
observers in batch processes**.

*Asia-Pacific Journal of Chemical Engineering*, Volume 9, Pages 81–92,
2014.

Tim Barfoot, Chi Hay Tong andbuk Simo Särkkä.

**Batch
Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process
Regression**.

In *Proceedings of Robotics: Science and Systems*, Berkeley, USA, 2014.

Felix Berkenkamp, Angela P. Schoellig.

**Learning-based
robust control: guaranteeing stability while improving performance**.

In *Proc. of the Machine Learning in Planning and Control of Robot Motion
Workshop at the IEEE/RSJ International Conference on Intelligent Robots and
Systems (IROS)*, 2014.

Hildo Bijl, Jan-Willem van Wingerden, Michel Verhaegen.

**Applying
Gaussian Processes to Reinforcement Learning for Fixed-Structure Controller
Synthesis**.

In *World Congress*, Volume 19, Pages 10391–10396, 2014.

Ilias Bilionis, Emil M Constantinescu, Mihai Anitescu.

**Data-driven model for
solar irradiation based on satellite observations**.

*Solar Energy*, Volume 110, Pages 22–38, 2014.

B. Bischoff, D. Nguyen-Tuong, H. van Hoof, A. McHutchon, C.E. Rasmussen, A.
Knoll, J. Peters, M.P. Deisenroth.

**Policy
Search For Learning Robot Control Using Sparse Data**.

In *Proceedings of 2014 IEEE International Conference on Robotics and
Automation (ICRA)*, 2014.

B. Bocsi, L. Csato, J. Peters.

**Indirect
Robot Model Learning for Tracking Control**.

*Advanced Robotics*, Volume 28, Pages 1–11, 2014.

B. Bocsi, H. Jakab, L. Csato.

**Simulation-Extrapolation
Gaussian Processes for Input Noise Modeling**.

In *Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2014
16th International Symposium on*, Pages 189–195, 2014.

Carl Boettiger, Marc Mangel, Stephan Munch.

**Avoiding
tipping points in fisheries management through Gaussian process dynamic
programming**.

*Proceedings of the Royal Society of London B: Biological Sciences*,
Volume 282, Pages n/a, 2014.

J. Boedecker, J. T. Springenberg, J. Wülfing, M. Riedmiller.

**Approximate
real-time optimal control based on sparse Gaussian process models**.

In *2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement
Learning (ADPRL)*, Pages 1–8, 2014.

Luca Bortolussi, Guido Sanguinetti.

**A
Statistical Approach for Computing Reachability of Non-linear and Stochastic
Dynamical Systems**.

*Quantitative Evaluation of Systems*, *Lecture Notes in Computer Science*,
2014.

Giulio Bottegal, Aleksandr Y Aravkin, Håkan Hjalmarsson, Gianluigi Pillonetto.

**Outlier robust system
identification: a Bayesian kernel-based approach**.

*IFAC Proceedings Volumes*, Volume 47, Pages 1073–1078, 2014.

W. Bukhari, S.-M. Hong.

**Real-time prediction
of respiratory motion using a cascade structure of an extended Kalman filter
and support vector regression**.

*Physics in Medicine and Biology*, 2014.

M.C. Burkhart, Y. Heo, V.M. Zavala.

**Measurement
and verification of building systems under uncertain data: A Gaussian process
modeling approach**.

*Energy and Buildings*, Volume 75, Pages 189–198, 2014.

Gang Cao, E.M.-K. Lai, F. Alam.

**Particle
swarm optimization for convolved Gaussian process models**.

In *Neural Networks (IJCNN), 2014 International Joint Conference on*,
Pages 1573–1578, 2014.

Francesca P. Carli.

**On the maximum entropy property
of the first-order stable spline kernel and its implications**.

In *Control Applications (CCA), 2014 IEEE Conference on*, Pages 409–414,
2014.

Hongmei Chen, Xianghong Cheng, Haipeng Wang, Xu Han.

**Dealing
with Observation Outages within Navigation Data using Gaussian Process
Regression**.

*Journal of Navigation*, Volume 67, Issue 04, Pages 603–615, 2014.

A. Chiuso, T. Chen, L. Ljung, G. Pillonetto.

**On the design of
multiple kernels for nonparametric linear system identification**.

In *53rd IEEE Conference on Decision and Control*, Pages 3346–3351, 2014.

Johan Dahlin, Fredrik Lindsten.

**Particle filter-based Gaussian
process optimisation for parameter inference**.

In *Proceedings of the 19th IFAC World Congress*, Pages 8675–8680, 2014.

M. P. Deisenroth, P. Englert, J. Peters, D. Fox.

**Multi-Task Policy Search
for Robotics**.

In *IEEE International Conference on Robotics and Automation (ICRA)*,
2014.

Bing Dong, KheePoh Lam.

**A real-time model
predictive control for building heating and cooling systems based on the
occupancy behavior pattern detection and local weather forecasting**.

*Building Simulation*, Volume 7, Pages 89–106, 2014.

C. Earls, G. Hooker.

**Bayesian
covariance estimation and inference in latent Gaussian process models**.

*Journal of Statistical Methodology*, Volume 18, Pages 79–100, 2014.

Roger Frigola, Yutian Chen, Carl Rasmussen.

**Variational
Gaussian Process State-Space Models**.

*Advances in Neural Information Processing Systems 27*, 2014.

Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen.

**Identification of Gaussian Process
State-Space Models with Particle Stochastic Approximation EM**.

In *Proceedings of the 19th World Congress of the International Federation of
Automatic Control (IFAC)*, Pages 4097–4102, 2014.

A. Golabi, N. Meskin, R. Tóth, J. Mohammadpour.

**A Bayesian approach for
estimation of linear-regression LPV models**.

In *53rd IEEE Conference on Decision and Control*, Volume , Pages
2555–2560, 2014.

Adam Gonczarek, Jakub M. Tomczak.

**Manifold
Regularized Particle Filter for Articulated Human Motion Tracking**.

*Advances in Systems Science*, *Advances in Intelligent Systems and
Computing*, 2014.

Robert C. Grande, Girish Chowdhary, Jonathan P. How.

**Experimental Validation of
Bayesian Nonparametric Adaptive Control Using Gaussian Processes**.

*J. Aerospace Inf. Sys.*, Volume 11, Pages 565–578, 2014.

Robert Grande, Thomas Walsh, Jonathan How.

**Sample Efficient
Reinforcement Learning with Gaussian Processes**.

In *Proceedings of the 31st International Conference on Machine Learning
(ICML-14)*, Pages 1332–1340, 2014.

T. Hachino, Y. Hashiguchi, H. Takata, S. Fukushima, Y. Igarashi.

**Local Gaussian process models for identification of discrete-time
Hammerstein Systems**.

*ICIC Express Letters*, Volume 8, Pages 173–179, 2014.

T. Hachino, K. Matsushita, H. Takata, S. Fukushima, Y. Igarashi.

**Identification
of continuous-time nonlinear systems via local Gaussian process models**.

*IEEJ Transactions on Electronics, Information and Systems*, Volume 134,
Pages 1708–1715, 2014.

Tomohiro Hachino, Hitoshi Takata, Shigeru Nakayama, Ichiro Iimura, Seiji
Fukushima, Yasutaka Igarashi.

**Gaussian Process Model
Identification Using Artificial Bee Colony Algorithm and Its Application to
Modeling of Power Systems**.

*International Journal of Electrical, Computer, Electronics and Communication
Engineering*, Volume 8, Pages 437 – 442, 2014.

Nooshin Haji-Ghassemi, Marc Deisenroth.

**Analytic
Long-Term Forecasting with Periodic Gaussian Processes**.

*Journal of Machine Learning Research*, Volume 33, Pages 303–311, 2014.

Ming Hu, Zonghai Sun.

**Multimodel
Nonlinear Predictive Control with Gaussian Process Model**.

*Unifying Electrical Engineering and Electronics Engineering*, *Lecture
Notes in Electrical Engineering*, 2014.

Marco F. Huber.

**Recursive
Gaussian process: On-line regression and learning**.

*Pattern Recognition Letters*, Volume 45, Pages 85–91, 2014.

He-ming Jia, Wen-long Song, Hong-wei Mu, Yan-ting Che.

**Research on
Initial Alignment Technology Based on GP-SRCDKF**.

*Computer Engineering*, Volume 40, Pages 195–198, 2014.

Sanket Kamthe, Jan Peters, Marc P. Deisenroth.

**Multi-Modal
Filtering for Non-linear Estimation**.

In *International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2014)*, 2014.

Juš Kocijan, Alexandra Grancharova.

**Application of
Gaussian Processes to the Modelling and Control in Process Engineering**.

*Innovations in Intelligent Machines-5*, *Studies in Computational
Intelligence*, 2014.

L. Král, J. Pruher, M. Šimandl.

**Gaussian process based
dual adaptive control of nonlinear stochastic systems**.

In *Control and Automation (MED), 2014 22nd Mediterranean Conference of*,
Pages 1074–1079, 2014.

Ivan Madjarov, Juš Kocijan, Alexandra Grancharova, Bogdan Shishedjiev.

**Towards
a Service-Based Framework for Environmental Data Processing**.

*International Journal of Advanced Computer Science and Applications(IJACSA)*,
Volume 5, Issue 4, 2014.

J. Mahler, S. Krishnan, M. Laskey, S. Sen, A. Murali, B. Kehoe, S. Patil,
Jiannan Wang, M. Franklin, P. Abbeel, K. Goldberg.

**Learning accurate
kinematic control of cable-driven surgical robots using data cleaning and
Gaussian Process Regression**.

In *Automation Science and Engineering (CASE), 2014 IEEE International
Conference on*, Pages 532–539, 2014.

L. Muñoz-González, M. Lázaro-Gredilla, Aníbal R. Figueiras-Vidal.

**Laplace
approximation with Gaussian Processes for volatility forecasting**.

In *Cognitive Information Processing (CIP), 2014 4th International Workshop
on*, Pages 1–6, 2014.

Gerhard Neumann, Christian Daniel, Alexandros Paraschos, Andras Kupcsik, Jan
Peters.

**Learning
Modular Policies for Robotics**.

*Frontiers in Computational Neuroscience*, Volume 8, Pages 1–13, 2014.

P. Ngo, J. Das, J. Ogle, J. Thomas, W. Anderson, R.N. Smith.

**Predicting the speed of
a Wave Glider autonomous surface vehicle from wave model data**.

In *Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International
Conference on*, Pages 2250–2256, 2014.

Wangdong Ni, Lars Nørgaard, Morten Mørup.

**Non-linear
calibration models for near infrared spectroscopy**.

*Analytica Chimica Acta*, Volume 813, Pages 1–14, 2014.

C.J. Ostafew, A.P. Schoellig, T.D. Barfoot.

**Learning-based
nonlinear model predictive control to improve vision-based mobile robot
path-tracking in challenging outdoor environments**.

In *Robotics and Automation (ICRA), 2014 IEEE International Conference on*,
Pages 4029–4036, 2014.

Dejan Petelin, Jus Kocijan.

**Evolving
Gaussian process models for predicting chaotic time-series**.

In *Evolving and Adaptive Intelligent Systems (EAIS), 2014 IEEE Conference on*,
Pages 1–8, 2014.

Gianluigi Pillonetto, Francesco Dinuzzo, Tianshi Chen, Giuseppe De Nicolao,
Lennart Ljung.

**Kernel
methods in system identification, machine learning and function estimation: A
survey**.

*Automatica*, Volume 50, Pages 657 – 682, 2014.

G. Prando, A. Chiuso, G. Pillonetto.

**Bayesian and regularization
approaches to multivariable linear system identification: The role of rank
penalties**.

In *53rd IEEE Conference on Decision and Control*, Pages 1482–1487, 2014.

Christian Preusche, Christoph Anger, Uwe Klingauf.

**Evaluation
of the Training Process of three different Prognostic Approaches based on the
Gaussian Process**.

In *Proceedings of the European conference of the prognostics and health
management society*, Pages 202–213, 2014.

Jakub Prüher, Miroslav Šimandl.

**Gaussian
process based recursive system identification**.

*Journal of Physics: Conference Series*, Volume 570, Pages 012002, 2014.

Steven Reece, Siddhartha Ghosh, Alex Rogers, Stephen Roberts, Nicholas R.
Jennings.

**Efficient State-space Inference
of Periodic Latent Force Models**.

*Journal of Machine Learning Research*, Volume 15, Pages 2337–2397, 2014.

M. Rupp, M. R. Bauer, R. Wilcken, A. Lange, M. Reutlinger, F. M. Boeckler, G.
Schneider.

**Machine
Learning Estimates of Natural Product Conformational Energies**.

*PLOS Computational Biology*, Volume 10, 2014.

Behrooz Safarinejadian, Elham Kowsari.

**Fault detection in
non-linear systems based on GP-EKF and GP-UKF algorithms**.

*Systems Science & Control Engineering*, Volume 2, Pages 610–620,
2014.

Ahmed Shokry, Antonio Espuña.

**Sequential
Dynamic Optimization of Complex Nonlinear Processes based on Kriging Surrogate
Models**.

*Procedia Technology*, Volume 15, Pages 376 - 387, 2014, 2nd International
Conference on System-Integrated Intelligence: Challenges for Product and
Production Engineering.

Arno Solin, Simo Särkkä.

**Explicit link
between periodic covariance functions and state space models**.

*Proceedings of the Seventeenth International Conference on Artificial
Intelligence and Statistics*, Volume 33, Pages 904–912, 2014.

Martin Stepančič, Juš Kocijan.

**Vodenje nestabilnega hidravličnega sistema z modelom na podlagi Gaussovih
procesov**.

*Industrijski forum IRT*, 2014, (in Slovene).

Martin Stepančič, Juš Kocijan.

**Prediktivno
vodenje nestabilnega sistema s sprotno identifikacijo verjetnostnega modela**.

*Ventil*, Volume 20, Issue 5, Pages 374–380, 2014, (in Slovene).

Zonghai Sun.

**Gaussian Process
adaptive control of nonlinear system base on online algorithm**.

In *Control Conference (CCC), 2014 33rd Chinese*, Pages 8791-8794, 2014.

A. Y. Sun, D. Wang, X. Xu.

**Monthly
streamflow forecasting using Gaussian Process Regression**.

*Journal of Hydrology*, Volume 511, Pages 72–81, 2014.

J.T. Thorson, K. Ono, S.B. Munch.

**A Bayesian
approach to identifying and compensating for model misspecification in
population models**.

*Ecology*, Volume 95, Pages 329–341, 2014.

Nils Tietze, Urich Konigorski, Duy Nguyen-Tuong.

**Local Gaussian
process regression for model-based calibration of engine control units**.

*5th Simulation and Testing for Automotive Electronics*, 2014.

Kenji Urai, Yuya Okadome, Yoshihiro Nakata, Yutaka Nakamura, Hiroshi Ishiguro.

**Estimation of physical
interaction between a musculoskeletal robot and its surroundings**.

*Artificial Life and Robotics*, Volume 19, Pages 193–200, 2014.

Dmytro Velychko, Dominik Endres, Nick Taubert, MartinA. Giese.

**Coupling
Gaussian Process Dynamical Models with Product-of-Experts Kernels**.

*Artificial Neural Networks and Machine Learning – ICANN 2014*, *Lecture
Notes in Computer Science*, 2014.

Yali Wang, Marcus A Brubaker, Brahim Chaib-draa, Raquel Urtasun.

**Bayesian
Filtering with Online Gaussian Process Latent Variable Models**.

*Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City,
Canada*, 2014.

Ziyou Wang, J. Kinugawa, Hongbo Wang, K. Kazahiro.

**A human motion
estimation method based on GP-UKF**.

In *Information and Automation (ICIA), 2014 IEEE International Conference on*,
Pages 1228–1232, 2014.

Keith Worden, Graeme Manson, ElizabethJ. Cross.

**On Gaussian Process
NARX Models and Their Higher-Order Frequency Response Functions**.

*Solving Computationally Expensive Engineering Problems*, *Springer
Proceedings in Mathematics & Statistics*, 2014.

Yulai Zhang, Guiming Luo, Fuan Pu.

**Power Load
Forecasting based on Multi-task Gaussian Process**.

In *Proceedings of the IFAC 19th World Congress*, Pages 3651–3656, 2014.

Jing Zhao, Shiliang Sun.

**Variational
Dependent Multi-output Gaussian Process Dynamical Systems**.

*Discovery Science*, *Lecture Notes in Computer Science*, 2014.

K. Zhou, G. Liang, J. Tang.

**Efficient model updating
using Bayesian probabilistic framework based on measured vibratory response**.

In *Nondestructive Characterization for Composite Materials, Aerospace
Engineering, Civil Infrastructure, and Homeland Security 2014*, Volume 9063,
2014.

Justina Žuraskiené, Paul Kirk, Thomas Thorne, John Pinney, Michael Stumpf.

**Derivative
processes for modelling metabolic fluxes**.

*Systems biology*, Volume 30, Pages 1892–1898, 2014.

**2013**

A Abusnina, D. Kudenko.

**Adaptive
Soft Sensor based on moving Gaussian process window**.

In *Industrial Technology (ICIT), 2013 IEEE International Conference on*,
Pages 1051–1056, 2013.

T. Alpcan, I Shames, M. Cantoni, G. Nair.

**Learning
and Information for Dual Control**.

In *Control Conference (ASCC), 2013 9th Asian*, Pages 1–6, 2013.

M.A Alvarez, D. Luengo, N.D. Lawrence.

**Linear
Latent Force Models Using Gaussian Processes**.

*Pattern Analysis and Machine Intelligence, IEEE Transactions on*, Volume
35, Pages 2693–2705, 2013.

Z. Amini, H. Rabbani.

**Seizure
diagnosis in children based on the electroencephalogram modellind by Gaussian
process model**.

*Journal of Isfahan Medical School*, Volume 31, Pages 985–996, 2013.

Erik Berger, David Vogt, Nooshin Haji-Ghassemi, Bernhard Jung, Heni Ben Amor.

**Inferring
Guidance Information in Cooperative Human-Robot Tasks**.

In *Proceedings of the International Conference on Humanoid Robots
(HUMANOIDS)*, 2013.

B. Bischoff, D. Nguyen-Tuong, H. Markert, A. Knoll.

**Learning
control under uncertainty: A probabilistic Value-Iteration approach**.

In *ESANN 2013 proceedings, 21st European Symposium on Artificial Neural
Networks, Computational Intelligence and Machine Learning*, Pages 209–214,
2013.

S. Butler, J. Ringwood, F. O'Connor..

**Exploiting
SCADA System Data for Wind Turbine Performance Monitoring**.

*Conference on Control and Fault-Tolerant Systems (SysTol) October 9-11,
2013. Nice, France*, 2013.

J. Calliess, M. Osborne, S. J. Roberts.

**Nonlinear
adaptive hybrid control by combining Gaussian process system identification
with classical control laws**.

In *Novel Methods for Learning and Optimization of Control Policies and
Trajectories for Robotics, ICRA, 2013*, 2013.

Pengfei Cao, Xionglin Luo.

**Modeling
of soft sensor for chemical process**.

*CIESC Journal*, 2013.

Lester Lik Teck Chan, Yi Liu, Junghui Chen.

**Nonlinear System
Identification with Selective Recursive Gaussian Process Models**.

*Industrial & Engineering Chemistry Research*, Volume 52, Pages
18276–18286, 2013.

Junghui Chen, Lester Lik Teck Chan, Yi-Cheng Cheng.

**Gaussian
process regression based optimal design of combustion systems using flame
images**.

*Applied Energy*, Volume 111, Pages 153–160, 2013.

Tianshi Chen, Lennart Ljung.

**Implementation
of algorithms for tuning parameters in regularized least squares problems in
system identification**.

*Automatica*, Volume 49, Pages 2213–2220, 2013.

N. Chen, Z. Qian, X. Meng.

**Multistep
Wind Speed Forecasting Based on Wavelet and Gaussian Processes**.

*Mathematical Problems in Engineering*, Volume 2013, 2013.

N. Chen, Z. Qian, X. Meng, I. T. Nabney.

**Short-Term Wind
Power Forecasting Using Gaussian Processes**.

*International joint conference on Artificial Intelligence IJCAI'13*,
2013.

K. Chen, Y. Zhang, J. Yi.

**Modeling
of Rider-Bicycle Interactions with Learned Dynamics on Constrained Embedding
Manifolds**.

*2013 IEEE/ASME International Conference on Advanced Intelligent
Mechatronics: Mechatronics for Human Wellbeing, AIM 2013*, 2013.

G. Chowdhary, H. Kingravi, J.P. How, P. Vela.

**Bayesian
nonparameteric model reference adaptive control using Gaussian processes**.

*52nd IEEE Conference on Decision and Control*, 2013.

G. Chowdhary, H.A. Kingravi, J.P. How, P.A. Vela.

**Bayesian
nonparametric adaptive control of time-varying systems using Gaussian processes**.

*Proceedings of the American Control Conference*, 2013.

Girish Chowdhary, Miao Liu, Robert C. Grande, Thomas J. Walsh, Jonathan P. How.

**Off-Policy
Reinforcement Learning with Gaussian Processes**.

*Multidisciplinary Conference on Reinforcement Learning and Decision Making*,
2013.

O.M. Cliff, T. Sildomar, Monteiro.

**Evaluating
Techniques for Learning a Feedback Controller for Low-Cost Manipulators**.

*IEEE International Conference on Intelligent Robots and Systems*, 2013.

M. Deisenroth, G. Neumann, J. Peters.

**A
Survey on Policy Search for Robotics**.

*Foundations and Trends in Robotics*, Volume 2, Issue 1-2, Pages 1–142,
2013.

A. H. ELSheikh, C. C. Pain, F. Fang, J. L. M. A. Gomes, I. M. Navon.

**Parameter
estimation of subsurface flow models using iterative regularized ensemble
Kalman filter**.

*Stochastic Environmental Research and Risk Assessment*, Volume 27, Pages
877–897, 2013.

Peter Englert, Alexandros Paraschos, Marc Deisenroth, Jan Peters.

**Probabilistic
Model-based Imitation Learning**.

*Adaptive Behavior*, Volume 21, Pages 388–403, 2013.

Peter Englert, Alexandros Paraschos, Jan Peters, Marc Peter Deisenroth.

**Model-based
Imitation Learning by Probabilistic Trajectory Matching**.

In *ICRA*, Pages 1922–1927, 2013.

Roger Frigola, Fredrik Lindsten, Thomas B Schon, Carl Rasmussen.

**Bayesian
Inference and Learning in Gaussian Process State-Space Models with Particle
MCMC**.

*Advances in Neural Information Processing Systems 26*, 2013.

Roger Frigola, Carl E Rasmussen.

**Integrated Pre-Processing for
Bayesian Nonlinear System Identification with Gaussian Processes**.

In *Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on*,
2013.

R.C. Grande, G. Chowdhary, J.P. How.

**Nonparametric
adaptive control using Gaussian Processes with online hyperparameter estimation**.

In *Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on*,
Pages 861–867, 2013.

Ratko Grbić, Dino Kurtagić, Dražen Slišković.

**Stream
water temperature prediction based on Gaussian process regression**.

*Expert Systems with Applications*, Volume Volume 40, Pages 7407–7414,
2013.

Ratko Grbić, Dražen Slišković, Petr Kadlec.

**Adaptive
soft sensor for online prediction and process monitoring based on a mixture of
Gaussian process models**.

*Computers & Chemical Engineering*, Volume 58, Pages 84–97, 2013.

Tomohiro Hachino, Hitoshi Takata, Shigeru Nakayama, Seiji Fukushima, Yasutaka
Igarashi.

**Application of
Firefly Algorithm to Gaussian Process-based Prediction of Electric Power Damage
Caused by Typhoons**.

*International Journal of Computer Science and Electronics Engineering
(IJCSEE)*, Volume 1, Issue 3, Pages 440–444, 2013.

Tomohiro Hachino, Tatsuya Ueda, Hitoshi Takata.

**Gaussian
Process Regression for Prediction of Electric Power Damage Caused by Typhoons
Considering Nonstationarity of Damage**.

*Journal of Signal Processing*, Volume 17, Issue 3, Pages 61–68, 2013.

M. Han, W. Ren, M. Xu.

**Prediction
of multivariate time series with sparse Gaussian process echo state network**.

*Proceedings of the 2013 International Conference on Intelligent Control and
Information Processing, ICICIP 2013*, 2013.

Prasad Hemakumara, Salah Sukkarieh.

**Learning
UAV Stability and Control Derivatives Using Gaussian Processes**.

*IEEE Transactions on Robotics*, Pages 1–12, 2013.

Sheng Hong, Zheng Zhou, Chuan Lv, Hongyi Guo.

**Prognosis
for insulated gate bipolar transistor based on Gaussian Process Regression**.

In *Prognostics and Health Management (PHM), 2013 IEEE Conference on*,
Pages 1–5, 2013.

M. F. Huber.

**Recursive
Gaussian process regression**.

In *2013 IEEE International Conference on Acoustics, Speech and Signal
Processing*, Pages 3362–3366, 2013.

E.D. Klenske, M.N. Zeilinger, B. Scholkopf, P. Hennig.

**Nonparametric
Dynamics Estimation for Time Periodic Systems**.

In *Communication, Control, and Computing (Allerton), 2013 51st Annual
Allerton Conference on*, Pages 486–493, 2013.

Juš Kocijan.

**Incorporating
knowledge about model structure in the identification of Gaussian-process
models**.

In *Recent Advances in Telecommunications, Signals and Systems*, Pages
124–129, Lemesos, Cyprus, 2013.

Peng Kou, Feng Gao, Xiaohong Guan.

**Sparse
online warped Gaussian process for wind power probabilistic forecasting**.

*Applied Energy*, Volume 108, Pages 410–428, 2013.

Andras Gabor Kupcsik, Marc Peter Deisenroth, Jan Peters, Gerhard Neumann.

**Data-Efficient
Generalization of Robot Skills with Contextual Policy Search**.

In *AAAI*, 2013.

Duehee Lee, Joonhyun Kim, R. Baldick.

**Stochastic
Optimal Control of the Storage System to Limit Ramp Rates of Wind Power Output**.

*Smart Grid, IEEE Transactions on*, Volume 4, Pages 2256–2265, 2013.

Yu Lei, Huizhong Yang.

**Combination
model soft sensor based on Gaussian process and Bayesian committee machine**.

*CIESC Journal*, Volume 64, Issue 12, Pages 4434–4438, 2013.

Xiangyu Li, Xianwen Gao, Yongbin Cui, Kun Li.

**Dynamic
liquid level modeling of sucker-rod pumping systems based on Gaussian process
regression**.

In *Natural Computation (ICNC), 2013 Ninth International Conference on*,
Pages 917–922, 2013.

Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan.

**Bayesian
semiparametric Wiener system identification**.

*Automatica*, Volume 49, Pages 2053–2063, 2013.

Zitao Liu, Lei Wu, Milos Hauskrecht.

**Modeling
clinical time series using Gaussian process sequences**.

In *SIAM international conference on data mining*, Pages 623–631, 2013.

J.M. Lourenço, J.M. Lemos, J.S. Marques.

**Control
of neuromuscular blockade with Gaussian process models**.

*Biomedical Signal Processing and Control*, Volume 8, Pages 244–254, 2013.

J.M. Maciejowski, X. Yang.

**Fault
tolerant control using Gaussian processes and model predictive control**.

*Conference on Control and Fault-Tolerant Systems (SysTol), Nice, France.*,
2013.

Hae Young Noh, Ram Rajagopal.

**Data-Driven
Forecasting Algorithms for Building Energy Consumption**.

In *Proceedings SPIE Sensors and Smart Structures Technologies for Civil,
Mechanical, and Aerospace Systems*, Volume 8692, San Diego, California, USA,
2013.

Sooho Park, S.K. Mustafa, K. Shimada.

**Learning-based
robot control with localized sparse online Gaussian process**.

In *Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International
Conference on*, Pages 1202–1207, 2013.

Sooho Park, S.K. Mustafa, K. Shimada.

**Learning
based robot control with sequential Gaussian process**.

In *Robotic Intelligence In Informationally Structured Space (RiiSS), 2013
IEEE Workshop on*, Pages 120–127, 2013.

Fernando Pérez-Cruz, Steven Van Vaerenbergh, Juan José Murillo-Fuentes, Miguel
Lázaro-Gredilla, Ignacio Santamaría.

**Gaussian
Processes for Nonlinear Signal Processing: An Overview of Recent Advances**.

*IEEE Signal Processing Magazine*, Volume 30, Pages 40–50, 2013.

L. Peternel, J. Babič.

**Learning
of compliant human-robot interaction using full-body haptic interface**.

*Advanced Robotics*, Volume 27, Pages 1003–1012, 2013.

Dejan Petelin, Alexandra Grancharova, Juš Kocijan.

**Evolving
Gaussian process models for prediction of ozone concentration in the air**.

*Simulation Modelling Practice and Theory*, Volume 33, Pages 68–80, 2013.

Gianluigi Pillonetto.

**Consistent
identification of Wiener systems: A machine learning viewpoint**.

*Automatica*, Volume 49, Pages 2704–2712, 2013.

G. Pillonetto, T. Chen, L. Ljung.

**Kernel-based model order
selection for linear system identification**.

*IFAC Proceedings Volumes (IFAC-PapersOnline)*, Volume 11, Pages 257–262,
2013.

Jan Přikryl.

**Graphics
card as a cheap supercomputer**.

In *Programs and Algorithms of Numerical Matematics*, Volume 16, Pages
162–167, 2013.

S. Särkkä, J. Hartikainen.

**Non-linear
noise adaptive Kalman filtering via variational Bayes**.

In *Machine Learning for Signal Processing (MLSP), 2013 IEEE International
Workshop on*, Pages 1–6, 2013.

Simo Särkkä, Arno Solin, Jouni Hartikainen.

**Spatiotemporal
Learning via Infinite-Dimensional Bayesian Filtering and Smoothing: A Look at
Gaussian Process Regression Through Kalman Filtering**.

*IEEE Signal Processing Magazine*, Volume 30, Pages 51–61, 2013.

S. Sedai, M. Bennamoun, D. Q. Huynh..

**A
Gaussian Process Guided Particle Filter for Tracking 3D Human Pose in Video**.

*IEEE Transactions on Image Processing*, Volume 22, Pages 4286 – 4300,
2013.

Gang Shen, Yu Cao.

**A Gaussian Process
Based Model Predictive Controller for Nonlinear Systems with Uncertain
Input-Output Delay**.

*Applied Mechanics and Materials*, Volume 433–435, Pages 1015–1020, 2013.

Arno Solin, Simo Särkkä.

**Infinite-dimensional
Bayesian filtering for detection of quasiperiodic phenomena in spatiotemporal
data**.

*Phys. Rev. E*, Volume 88, Issue 5, Pages 052909, 2013.

Yanyu Su, Yan Wu, Harold Soh, Zhijiang Du, Yiannis Demiris.

**Enhanced
Kinematic Model for Dexterous Manipulation with an Underactuated Hand**.

In *IROS*, Pages 2493–2499, 2013.

Taiji Suzuki, Kazuyuki Aihara.

**Nonlinear
system identification for prostate cancer and optimality of intermittent
androgen suppression therapy**.

*Mathematical biosciences*, 2013.

N. Taubert, M. Löffler, N. Ludolph, A. Christensen, D. Endres, M.A. Giese.

**A
virtual reality setup for controllable, stylized real-time interactions between
humans and avatars with sparse Gaussian process dynamical models**.

In *Proceedings - SAP 2013: ACM Symposium on Applied Perception*, Pages
41–44, 2013.

V. Vitelli, E. Zio.

**Approximate Gaussian Process
Regression with Sparse Functional Learning of Inducing Points for Components
Condition Monitoring**.

*Chemical Engineering Transactions*, Volume 33, Pages 907–912, 2013.

Y. Wang, B. Chaib-Draa.

**A KNN based
kalman filter Gaussian process regression**.

*International joint conference on Artificial Intelligence IJCAI'13*,
2013.

Zhikun Wang, Katharina Mülling, Marc Peter Deisenroth, Heni Ben Amor, David
Vogt, Bernhard Schölkopf, Jan Peters.

**Probabilistic
Movement Modeling for Intention Inference in Human-Robot Interaction**.

*The International Journal of Robotics Research*, Volume 32, Pages
841–858, 2013.

Z. Xia, J. Tang.

**Characterization
of Dynamic Response of Structures With Uncertainty by Using Gaussian Processes**.

*Journal of Vibration and Acoustics-Transactions of the ASME*, Volume 135,
Issue 5, 2013.

Wei Xi-qing, Song Shen-min.

**Model-free
cubature Kalman filter and its application**.

*Control and Decision*, Volume 28, 2013.

Juan Yan, Kang Li, Er-Wei Bai.

**Prediction
error adjusted Gaussian Process for short-term wind power forecasting**.

In *Intelligent Energy Systems (IWIES), 2013 IEEE International Workshop on*,
Pages 173–178, 2013.

J. Yu, K. Chen, J. Mori, M. M. Rashid.

**A
Gaussian mixture copula model based localized Gaussian process regression
approach for long-term wind speed prediction**.

*Energy*, Volume 61, Pages 673–686, 2013.

J. Yu, K. Chen, J. Mori, M.M. Rashid.

**Multi-kernel
Gaussian process regression and Bayesian model averaging based nonlinear state
estimation and quality prediction of multiphase batch processes**.

*Proceedings of the American Control Conference*, 2013.

J. Yu, K. Chen, M. M. Rashid.

**A
Bayesian model averaging based multi-kernel Gaussian process regression
framework for nonlinear state estimation and quality prediction of multiphase
batch processes with transient dynamics and uncertainty**.

*Chemical Engineering Science*, Volume 93, Pages 96–109, 2013.

**2012**

Christoph Anger, R Schrader, U Klingauf.

**Unscented
Kalman filter with Gaussian process degradation model for bearing fault
prognosis**.

In *Proceedings of the European conference of the prognostics and health
management society*, Pages 202–213, 2012.

M. M. Atia, A. Noureldin, M. Korenberg.

**Enhanced
Kalman Filter for RISS/GPS Integrated Navigation using Gaussian Process
Regression**.

In *Proceedings of the 2012 International Technical Meeting of The Institute
of Navigation*, Pages 1148–1156, Newport Beach, California, USA, 2012.

Er-Wei Bai.

**Local
Prediction Error Adjusted Gaussian Process for Nonlinear Non-Parametric System
Identification**.

In *16th IFAC Symposium on System Identification*, Pages 101–106,
Brussels, Belgium, 2012.

Tianshi Chen, Henrik Ohlsson, Lennart Ljung.

**On the
estimation of transfer functions, regularizations and Gaussian
processes-Revisited**.

*Automatica*, Volume 48, Pages 1525–1535, 2012.

Alessandro Chiuso, Gianluigi Pillonetto.

**A
Bayesian approach to sparse dynamic network identification**.

*Automatica*, Volume 48, Pages 1553–1565, 2012.

Girish Chowdhary, Jonathan How, Hassan Kingravi.

**Model
reference adaptive control using nonparametric adaptive elements**.

In *Conference on Guidance Navigation and Control, Minneapolis, MN*, 2012.

L. Clifton, D. A. Clifton, M. A. F. Pimentel, P. J. Watkinson, L. Tarassenko.

**Gaussian
Process Regression in Vital-Sign Early Warning Systems**.

*Engineering in Medicine and Biology Society (EMBC), 2012 Annual
International Conference of the IEEE*, 2012.

John P Cunningham, Zoubin Ghahramani, Carl E Rasmussen.

**Gaussian
Processes for time-marked time-series data**.

*Journal of Machine Learning Research - Proceedings Track*, Volume 22,
Pages 225–263, 2012.

A.C. Damianou, C.H. Ek, M.K. Titsias, N.D. Lawrence.

**Manifold relevance determination**.

In *Proceedings of the 29th International Conference on Machine Learning
(ICML)*, Volume 1, Pages 145–152, 2012.

M. De Paula, E. Martinez.

**Probabilistic
optimal control of blood glucose under uncertainty**.

*Computer Aided Chemical Engineering*, Volume 30, Pages 1357–1361, 2012.

Marc Peter Deisenroth, Roberto Calandra, André Seyfarth, Jan Peters.

**Toward
Fast Policy Search for Learning Legged Locomotion**.

In *IROS'12*, Pages 1787–1792, 2012.

Marc Peter Deisenroth, Shakir Mohamed.

**Expectation
Propagation in Gaussian Process Dynamical Systems**.

*Advances in Neural Information Processing Systems*, Volume 25, Pages
2618–2626, 2012.

MP Deisenroth, J Peters.

**Solving
Nonlinear Continuous State-Action-Observation POMDPs for Mechanical Systems
with Gaussian Noise**.

In *European Workshop on Reinforcement Learning (EWRL 2012)*, Pages 1–14,
2012.

Marc Peter Deisenroth, Ryan Darby Turner, Marco F. Huber, Uwe D. Hanebeck, Carl
Edward Rasmussen.

**Robust
Filtering and Smoothing with Gaussian Processes**.

*IEEE Transactions on Automatic Control*, Volume 57, Pages 1865–1871,
2012.

Dingwen Dong.

**Mine
Gas Emission Prediction based on Gaussian Process Model**.

*Procedia Engineering*, Volume 45, Pages 334–338, 2012.

D.-W. Dong, S.-G. Li, X.-T. Chang, H.-F. Lin.

**Prediction
model of gas concentration around working face using multivariate time series**.

*Journal of Mining and Safety Engineering*, Volume 29, Pages 135–139,
2012.

R. Grbić, D. Slišković, P. Kadlec.

**Adaptive
soft sensor for online prediction based on moving window Gaussian process
regression**.

In *2012 11th International Conference on Machine Learning and Applications*,
Pages 428–433, 2012.

Gregor Gregorčič, Gordon Lightbody.

**Gaussian
process internal model control**.

*International Journal of Systems Science*, Volume 43, Pages 2079–2094,
2012.

Tobias Gutjahr, Holger Ulmer, Christoph Ament.

**Sparse
Gaussian Processes with Uncertain Inputs for Multi-Step Ahead Prediction**.

In *16th IFAC Symposium on System Identification*, Pages 107–112, Brussels,
Belgium, 2012.

Tomohiro Hachino, Shoichi Yamakawa.

**Non-parametric
identification of continuous-time Hammerstein systems using Gaussian process
model and particle swarm optimization**.

*Artificial Life and Robotics*, Volume 17, Pages 35–40, 2012.

J. Hall, C. E. Rasmussen, J. Maciejowski.

**Modelling and
Control of Nonlinear Systems using Gaussian Processes with Partial Model
Information**.

*Conference on Decision and Control (CDC)*, 2012.

Jouni Hartikainen, Mari Seppänen, Simo Särkkä.

**State-Space Inference for
Non-Linear Latent Force Models with Application to Satellite Orbit Prediction**.

In *Proceedings of the 29th International Conference on Machine Learning
(ICML-12)*, Pages 903–910, Edinburgh, Scotland, 2012.

Y. Heo, V. M. Zavala.

**Gaussian process
modeling for measurement and verification of building energy savings**.

*Energy and buildings*, Volume 53, Pages 2012, 2012.

Živko Južnič-Zonta, Juš Kocijan, Xavier Flotats, Darko Vrečko.

**Multi-criteria
analyses of wastewater treatment bio-processes under an uncertainty and a
multiplicity of steady states**.

*Water research*, Volume 46, Pages 6121–31, 2012.

Hyuk Kang, F. C. Park.

**Humanoid
Motion Optimization via Nonlinear Dimension Reduction**.

*2012 IEEE International Conference on Robotics and Automation*, Pages
1444–1449, 2012.

Juš Kocijan.

**Dynamic
GP models: an overview and recent developments**.

In *ASM'12 Proceedings of the 6th international conference on Applied
Mathematics, Simulation, Modelling*, Pages 38–43, 2012.

Christophe Lecomte, J J Forster, B R Mace, N S Ferguson.

**Bayesian
Damage Localisation at Higher Frequencies with Gaussian Process Error**.

In *Conference Proceedings of the Society for Experimental Mechanics Series*,
Pages 39–48, New York, New York, USA, 2012.

Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan.

**A
semiparametric Bayesian approach to Wiener system identification**.

In *16th IFAC Symposium on System Identification*, Pages 1137–1142,
Brussels, Belgium, 2012.

M. Niranjan W. Liu.

**Gaussian
process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile**.

*Bioinformatics*, Volume 28, Pages 366–372, 2012.

Zaobao Liu, Weiya Xu, Jianfu Shao.

**Gauss
Process Based Approach for Application on Landslide Displacement Analysis and
Prediction**.

*CMES - Computer Modeling in Engineering and Sciences*, Volume 84, Pages
99–122, 2012.

J. M. Lourenco, P. J. Santos.

**Short-term
load forecasting using a Gaussian process model: The influence of a
derivativeterm in the input regressor**.

*Intelligent Decision Technologies*, Volume 6, 2012.

Max D. Morris.

**Gauss
Process Based Approach for Application on Landslide Displacement Analysis and
Prediction**.

*Technometrics*, Volume 54, Pages 42–50, 2012.

Duy Nguyen-Tuong, J. Peters.

**Online
Kernel-Based Learning for Task-Space Tracking Robot Control**.

*Neural Networks and Learning Systems, IEEE Transactions on*, Volume 23,
Pages 1417–1425, 2012.

Wangdong Ni, Soon Keat Tan, Wun Jern Ng, Steven D. Brown.

**Moving-Window GPR
for Nonlinear Dynamic System Modeling with Dual Updating and Dual Preprocessing**.

*Industrial & Engineering Chemistry Research*, Volume 51, Pages
6416–6428, 2012.

Wangdong Ni, Ke Wang, Tao Chen, Wun Jern Ng, Soon Keat Tan.

**GPR
model with signal preprocessing and bias update for dynamic processes modeling**.

*Control Engineering Practice*, Volume 20, Pages 1281–1292, 2012.

Michael A. Osborne, Roman Garnett, Kevin Swersky, Nando de Freitas.

**Prediction and Fault Detection
of Environmental Signals with Uncharacterised Faults**.

In *26th AAAI Conference on Artificial Intelligence (AAAI-12)*, Toronto,
Canada, 2012.

Carl Edward Rasmussen.

**Machine
Learning, Probabilistic Inference, System Identification and Control**.

In *16th IFAC Symposium on System Identification*, Pages 1275–1275, 2012.

Simo Särkkä, Jouni Hartikainen.

**Infinite-Dimensional
Kalman Filtering Approach to Spatio-Temporal Gaussian Process Regression**.

*Journal of Machine Learning Research - Proceedings Track*, Volume 22,
Pages 993–1001, 2012.

Harold Soh, Yiannis Demiris.

**Iterative
Temporal Learning and Prediction with the Sparse Online Echo State Gaussian
Process**.

In *The 2012 International Joint Conference on Neural Networks (IJCNN)*,
Pages 1–8, 2012.

B. Sun, H. Yao, T. Liu.

**Short-term wind
speed forecasting based on Gaussian process regression model**.

In *Proceedings of the Chinese Society of Electrical Engineering*, 2012.

Chi Hay Tong, P. Furgale, T.D. Barfoot.

**Gaussian Process
Gauss-Newton: Non-Parametric State Estimation**.

In *Computer and Robot Vision (CRV), 2012 Ninth Conference on*, Pages
206–213, 2012.

Ryan Turner, Carl Edward Rasmussen.

**Model based learning
of sigma points in unscented Kalman filtering**.

*Neurocomputing*, Volume 80, Pages 47–53, 2012.

Yali Wang, Brahim Chaib-draa.

**A
Marginalized Particle Gaussian Process Regression**.

In *Neural Information Processing Systems 25*, 2012.

Yali Wang, Brahim Chaib-draa.

**An
Adaptive Nonparametric Particle Filter for State Estimation**.

In *2012 IEEE International Conference on Robotics and Automation*, Pages
4355–4360, 2012.

Zhikun Wang, Marc Deisenroth, Heni Ben Amor, David Vogt, Bernhard Scholkopf,
Jan Peters.

**Probabilistic
Modeling of Human Movements for Intention Inference**.

In *Proceedings of Robotics: Science and Systems*, Sydney, Australia,
2012.

Jie Yu.

**Online
quality prediction of nonlinear and non-Gaussian chemical processes with shifting
dynamics using finite mixture model based Gaussian process regression approach**.

*Chemical Engineering Science*, Volume 82, Pages 22–30, 2012.

Zulkarnain Zainudin, Sarath Kodagoda, Linh Van Nguyen.

**Mutual
information based data selection in Gaussian processes for people tracking**.

In *Proceedings of Australasian Conference on Robotics and Automation*,
Pages 225–230, 2012.

**2011**

Etienne Rudolph Ackermann, Johan Pieter de Villiers, Pierre J. Cilliers.

**Nonlinear
dynamic systems modeling using Gaussian processes: Predicting ionospheric total
electron content over South Africa**.

*Journal of Geophysical Research A: Space Physics*, Volume 116, 2011.

Mauricio A. Álvarez, Neil D. Lawrence.

**Computationally
Efficient Convolved Multiple Output Gaussian Processes**.

*Journal of Machine Learning Research*, Volume 12, Pages 1459–1500, 2011.

Kristjan Ažman, Juš Kocijan.

**Dynamical systems
identification using Gaussian process models with incorporated local models**.

*Engineering Applications of Artificial Intelligence*, Volume 24, Pages
398–408, 2011.

William Becker, Keith Worden, Manuela Battipede, Cecilia Surace.

**Uncertainty
Analysis of a Dynamic Model of a Novel Remotely Piloted Airship**.

*Journal of Aircraft*, Volume 48, Pages 1028–1035, 2011.

Sotirios P. Chatzis, Yiannis Demiris.

**Echo
state Gaussian process**.

*IEEE Transactions on Neural Networks*, Volume 22, Pages 1435–1445, 2011.

Tianshi Chen, Henrik Ohlsson, Lennart Ljung.

**On
the Estimation of Transfer Functions, Regularizations and Gaussian Processes -
Revisited**.

In *18th IFAC World Congress*, 2011.

Marc Peter Deisenroth, Henrik Ohlsson.

**A
general perspective on Gaussian filtering and smoothing: Explaining current and
deriving new algorithms**.

In *American Control Conference (ACC), 2011*, Pages 1807–1812, 2011.

Marc P Deisenroth, Carl Edward Rasmussen.

**PILCO: A model-based and
data-efficient approach to policy search**.

In *International Conference on Machine Learning (ICML)*, Pages 465–472,
2011.

Marc P Deisenroth, Carl Edward Rasmussen, Dieter Fox.

**Learning to Control a
Low-Cost Manipulator using Data-Efficient Reinforcement Learning**.

In *Robotics: Science & Systems (RSS)*, 2011.

Guolliang Fan, Xin Zhang, Meng Ding.

**Gaussian
process for human motion modeling: A comparative study**.

*IEEE International Workshop on Machine Learning for Signal Processing*,
2011.

Nuwan Gamage, Ye Chow Kuang, Rini Akmeliawati, Serge Demidenko.

**Gaussian
Process Dynamical Models for hand gesture interpretation in Sign Language**.

*Pattern Recognition Letters*, Volume 32, Pages 2009–2014, 2011.

Zhiqiang Ge, Tao Chen, Zhihuan Song.

**Quality
prediction for polypropylene production process based on CLGPR model**.

*Control Engineering Practice*, Volume 19, Pages 423–432, 2011.

Alexandra Grancharova, Juš Kocijan.

**Explicit stochastic model
predictive control of gas-liquid separator based on Gaussian process model**.

In *Proceedings of the International Conference Automatics and Informatics
2011*, 2011.

Perry Groot, Peter Lucas, Paul van den Bosch.

**Multiple-step
time series forecasting with sparse Gaussian processes**.

In *Proceedings of the 23rd Benelux Conference on Artificial Intelligence
(BNAIC 2011)*, Pages 105–112, Ghent, 2011.

Tomohiro Hachino, Visakan Kadirkamanathan.

**Multiple
Gaussian process models for direct time series forecasting**.

*IEEJ Transactions on Electrical and Electronic Engineering*, Volume 6,
Pages 245–252, 2011.

Joseph Hall, Carl Edward Rasmussen, Jan Maciejowski.

**Reinforcement
Learning with Reference Tracking Control in Continuous State Spaces**.

In *Proceedings of the 50th International Conference on Decision and Control*,
2011.

Jouni Hartikainen, Jaakko Riihimäki, Simo Särkkä.

**Sparse
Spatio-temporal Gaussian Processes with General Likelihoods**.

*Artificial Neural Networks and Machine Learning - ICANN 2011*, *Lecture
Notes in Computer Science*, 2011.

Jouni Hartikainen, Simo Särkkä.

**Sequential
Inference for Latent Force Models**.

*Proceedings of the 27th Conference on Uncertainty in Artificial
Intelligence, UAI 2011*, 2011.

Philipp Hennig.

**Optimal
reinforcement learning for Gaussian systems**.

In *Advances in Neural Information Processing Systems*, Pages 325–333,
2011.

Andres Felipe Hernandez, Martha Grover.

**Comparison of
Sampling Strategies for Gaussian Process Models, with Application to
Nanoparticle Dynamics**.

*Industrial and Engineering Chemistry Research*, Volume 50, Pages
1379–1388, 2011.

Antti Honkela, Pei Gao, Jonatan Ropponen, Magnus Rattray, Neil D. Lawrence.

**tigre:
Transcription factor inference through Gaussian process reconstruction of
expression for bioconductor**.

*Bioinformatics*, Volume 27, Pages 1026–1027, 2011.

Wenjing Huang, Ke Wang, F. Jay Breidt, Richard A. Davis.

**A
class of stochastic volatility models for environmental applications**.

*Journal of Time Series Analysis*, Volume 32, Pages 364–377, 2011.

Hunor Jakab, Lehel Csató.

**Improving Gaussian
Process Value Function Approximation in Policy Gradient Algorithms**.

*Artificial Neural Networks and Machine Learning - ICANN 2011*, *Lecture
Notes in Computer Science*, 2011.

Đani Juričić, Pavel Ettler, Juš Kocijan.

**Fault
detection based on Gaussian process models: An application to the rolling mill**.

*ICINCO 2011 - Proceedings of the 8th International Conference on Informatics
in Control, Automation and Robotics*, 2011.

A. A. Kalaitzis, N. D. Lawrence.

**A
Simple Approach to Ranking Differentially Expressed Gene Expression Time
Courses through Gaussian Process Regression**.

*BMC Bioinformatics*, Volume 12, 2011.

Jonathan Ko, Dieter Fox.

**Learning
GP-BayesFilters via Gaussian process latent variable models**.

*Autonomous Robots*, Volume 30, Pages 3–23, 2011.

Juš Kocijan.

**Control Algorithms Based on
Gaussian Process Models: A State-of-the-Art Survey**.

In *Proceedings of the Special International Conference on Complex Systems:
Synergy of Control, Communications and Computing - COSY 2011*, 2011.

Juš Kocijan, Dejan Petelin.

**Output-Error Model
Training for Gaussian Process Models**.

*Adaptive and Natural Computing Algorithms*, *Lecture Notes in Computer
Science*, 2011.

Michał Lewandowski, Dimitros Makris, Jean-Christoph Nebel.

**Probabilistic
Feature Extraction from Multivariate Time Series using Spatio-Temporal
Constraints**.

*Lecture Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics)*, Volume 6635
LNAI, Pages 173–184, 2011.

Andrew McHutchon, Carl Edward Rasmussen.

**Gaussian Process Training with Input Noise**.

*Advances in Neural Information Processing Systems*, 2011.

O. Menzer, A. Moffat, G. Lasslop, M. Reichstein.

**Gaussian Process
Regression for Uncertainty Estimation on Ecosystem Data**.

*American Geophysical Union, Fall Meeting*, 2011.

Duy Nguyen-Tuong, Jan Peters.

**Incremental
online sparsification for model learning in real-time robot control**.

*Neurocomputing*, Volume 74, Pages 1859–1867, 2011.

Wangdong Ni, Soon Keat Tan, Wun Jern Ng.

**Recursive
GPR for nonlinear dynamic process modeling**.

*Chemical Engineering Journal*, Volume 173, Pages 636–643, 2011.

PnichHang Ou, Hengshan Wang.

**Modeling
and forecasting stock market volatility by Gaussian processes based on GARCH,
EGARCH and GJR models**.

*Proceedings of the World Congress on Engineering 2011, WCE 2011*, 2011.

Jóan Petur Petersen, Daniel J Jacobsen, Ole Winther.

**Statistical
modelling for ship propulsion efficiency**.

*Journal of Marine Science and Technology*, Pages 1–10, 2011.

Dejan Petelin, Juš Kocijan.

**Control
system with evolving Gaussian process models**.

*IEEE SSCI 2011: Symposium Series on Computational Intelligence - EAIS 2011:
2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems*, 2011.

Dejan Petelin, Juš Kocijan, Alexandra Grancharova.

**On-line
Gaussian process model for the prediction of the ozone concentration in the air**.

*Comptes Rendus de L'Academie Bulgare des Sciences*, Volume 64, Pages
117–124, 2011.

D Petelin, J Sindelar, J Prikryl, J Kocijan.

**Financial
modeling using Gaussian process models**.

In *Intelligent Data Acquisition and Advanced Computing Systems (IDAACS),
2011 IEEE 6th International Conference on*, Volume 2, Pages 672–677, 2011.

Gianluigi Pillonetto, Alessandro Chiuso, Giuseppe De Nicolao.

**Prediction
error identification of linear systems: A nonparametric Gaussian regression
approach**.

*Automatica*, Volume 47, Pages 291–305, 2011.

Gianluigi Pillonetto, Minh Ha Quang, Alessandro Chiuso.

**A New
Kernel-Based Approach for NonlinearSystem Identification**.

*IEEE Transactions on Automatic Control*, Volume 56, Pages 2825–2840,
2011.

Shi Qu, Ronghuan Yu, Yingmei Wei, Lingda Wu.

**Gaussian
Process Latent Variable Models for Inverse Kinematics**.

*Journal of Multimedia*, Volume 6, Pages 48–55, 2011.

Aditi Roy, Shamik Sural, Jayanta Mukherjee, Gerhard Rigoll.

**Occlusion
detection and gait silhouette reconstruction from degraded scenes**.

*Signal, Image and Video Processing*, Volume 5, Pages 415–430, 2011.

Javier Serradilla, Jian Qing Shi, Julian A. Morris.

**Fault
detection based on Gaussian process latent variable models**.

*Chemometrics and Intelligent Laboratory Systems*, Volume 109, Pages 9–21,
2011.

Jian Qing Shi, Taeryon Choi.

**Gaussian
Process Regression Analysis for Functional Data**.

Taylor and Francis, 2011.

Xiaolin Wei, Jianyuan Min, Jinxiang Chai.

**Physically-Valid
Statistical Models for Human Motion Generation**.

*ACM Transactions on Graphics*, Volume 30, 2011.

Z. Xia, J. Tang.

**Characterization
of Structural Dynamics With Uncertainty by Using Gaussian Processes**.

*ASME 2011 International Design Engineering Technical Conferences and
Computers and Information in Engineering Conference*, 2011.

Hongkai Xiong, Zhe Yuan, Yuan F. Zheng.

**A
learning-based video compression on low-quality data by unscented kalman
filters with Gaussian process regression**.

*Proceedings - IEEE International Symposium on Circuits and Systems*, 2011.

Jianfeng Xu, Koichi Takagi, Shigeyuki Sakazawa.

**Human motion
tracking with monocular video by introducing a graph structure into Gaussian
process dynamical models**.

*Lecture Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics)*, Volume 7087
LNCS, Pages 370–383, 2011.

Wenjin Yan, Shuangquan Hu, Yanhui Yang, Furong Gao, Tao Chen.

**Bayesian
migration of Gaussian process regression for rapid process modeling and
optimization**.

*Chemical Engineering Journal*, Volume 166, Pages 1095–1103, 2011.

Fu Yongfeng.

**A
dynamic soft-sensor modeling method based on FC-GP for 4-CBA content**.

*Conference Record - IEEE Instrumentation and Measurement Technology
Conference*, 2011.

Xu Zhao, Yun Fu, Yuncai Liu.

**Human
motion tracking by temporal-spatial local Gaussian process experts**.

*IEEE Transactions on Image Processing*, Volume 20, Pages 1141–1151, 2011.

Ze Zhang, Tuopeng Tong, Kai Song.

**A
novel GPLS-GP algorithm and its application to air temperature prediction**.

*Proceedings - 2011 7th International Conference on Natural Computation, ICNC
2011*, 2011.

**2010**

Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence.

**Efficient
Multioutput Gaussian Processes through Variational Inducing Kernels**.

*Journal of Machine Learning Research - Proceedings Track*, Volume 9,
Pages 25–32, 2010.

Mauricio A. Álvarez, Jan Peters, Bernhard Schölkopf, Neil D. Lawrence.

**Switched
Latent Force Models for Movement Segmentation**.

In *Advances in Neural Information Processing Systems 23: 24th Annual
Conference on Neural Information Processing Systems 2010. Proceedings of a
meeting held 6-9 December 2010, Vancouver, British Columbia, Canada*, Pages
55–63, 2010.

Cédric Archambeau, Manfred Opper.

**Approximate
inference for continuous-time Markov processes**.

*Inference and Learning in Dynamic Models*, 2010.

Liefeng Bo, Cristian Sminchisescu.

**Twin
Gaussian processes for structured prediction**.

*International Journal of Computer Vision*, Volume 87, Pages 28–52, 2010.

Satish T. S. Bukkapatnam, Changqing Cheng.

**Forecasting the
evolution of nonlinear and nonstationary systems using recurrence-based local
Gaussian process models**.

*Physical Review E*, Volume 82, 2010.

Salil Deena, Shaobo Hou, Aphrodite Galata.

**Visual
Speech Synthesis by Modelling Coarticulation Dynamics using a Non-Parametric
Switching State-Space Model**.

In *International Conference on Multimodal Interfaces and the Workshop on
Machine Learning for Multimodal Interaction (ICMI-MLMI 2010)*, 2010.

Marc Peter Deisenroth, Henrik Ohlsson.

**A
Probabilistic Perspective on Gaussian Filtering and Smoothing**.

*ArXiv e-prints*, 2010.

Marc Peter Deisenroth, Carl Edward Rasmussen.

**A Practical
and Conceptual Framework for Learning in Control**.

Technical Report UW-CSE-10-06-01, Department of Computer Science &
Engineering, University of Washington, Seattle, 2010.

Marc Peter Deisenroth, Carl Edward Rasmussen.

**Reducing Model
Bias in Reinforcement Learning**.

In *Learning and Planning from Batch Time Series Data*, 2010.

Dragoljub Gagi Drmanac, Brendon Bolin, Li-C. Wang.

**A
Non-Parametric Approach to Behavioral Device Modeling**.

In *1th International Symposium on Quality of Electronic Design (ISQED)*,
Pages 284–290, San Jose, CA, 2010.

Denis Forte, Aleš Ude, Andrej Kos.

**Robot
learning by Gaussian process regression**.

In *Proceedings of the 19th International Workshop on Robotics in
Alpe-Adria-Danube Region (RAAD 2010)*, Pages 303–308, 2010.

Alexandra Grancharova, Juš Kocijan, Alexander Krastev, Hristina Hristova.

**High-order
Gaussian process models for prediciton of ozone concentration in the air**.

In *Proceedings of the 7th EUROSIM Congress on Modelling and Simulation
EUROSIM 2010*, Volume 2, Pages 8, Prague, 2010.

Raia Hadsell, J. Andrew Bagnell, Daniel F. Huber, Martial Hebert.

**Non-Stationary
Space-Carving Kernels for Accurate Rough Terrain Estimation**.

*International Journal of Robotics Research*, Volume 29, Pages 981–996,
2010.

Andres Felipe Hernandez, Martha Grover.

**Stochastic
dynamic predictions using Gaussian process models for nanoparticle synthesis**.

*Computers and Chemical Engineering*, Volume 34, Pages 1953–1961, 2010.

Antti Honkela, Charles Girardot, E. Hilary Gustafson, Ya-Hsin Liu, E. M.
Furlong Furlong, Neil D. Lawrence, Magnus Rattray.

**Model-based method for
transcription factor target identification with limited data**.

*Proceedings of the National Academy of Sciences of the United States of
America*, Volume 107, Pages 7793–7798, 2010.

X. Jiang, B. Donga, L. Xie, L. Sweeney.

**Adaptive
Gaussian Process for Short-Term Wind Speed Forecasting**.

In *Proceedings of the 2010 conference on ECAI 2010: 19th European Conference
on Artificial Intelligence*, Pages 661–666, 2010.

T. Jung, P. Stone.

**Gaussian
processes for sample efficient reinforcement learning with RMAX-like
exploration**.

*Lecture Notes in Computer Science*, Volume 6321, Pages 601–616, 2010.

Juš Kocijan, Alexandra Grancharova.

**Gaussian
process modelling case study with multiple outputs**.

*Comptes Rendus de l Academie Bulgare des Sciences*, Volume 36, Pages
601–607, 2010.

Juš Kocijan, Jan Prikryl.

**Soft
Sensor for Faulty Measurements Detection and Reconstruction in Urban Traffic**.

In *Proceedings 15th IEEE Mediterranian Electromechanical Conference
(MELECON)*, Pages 172–177, Valletta, Malta, 2010.

Peng Li, Shen-min Song, Xing-lin Chen, Guang-ren Duan.

**Square root
unscented Kalman filter incorporating Gaussian process regression**.

*Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics*,
Volume 32, Pages 1281–1285, 2010.

João Lourenço, Paulo J. Santos.

**Short term load
forecasting using Gaussian process models**.

Technical Report ISSN: 1645-2631, Issue 5, Instituto de Engenharia de Sistemas
e Computadores de Coimbra Institute of Systems Engineering and Computers INESC,
Coimbra, 2010.

Bojan Musizza, Dejan Petelin, Juš Kocijan.

**Accelerated learning of
Gaussian process models**.

In *Proceedings of the 7th EUROSIM Congress on Modelling and Simulation*,
2010.

J. C. Nascimento, J. G. Silva.

**Manifold
Learning for Object Tracking with Multiple Motion Dynamics**.

*Lecture Notes in Computer Science*, Volume 6313, Pages 172–185, 2010.

Duy Nguyen-Tuong, Jan Peters.

**Using
Model Knowledge for Learning Inverse Dynamics**.

In *Proceedings of IEEE International Conference on Robotics and Automation*,
Pages 2677–2682, 2010.

Duy Nguyen-Tuong, Matthias Seeger, Jan Peters.

**Real-time
local GP model learning**.

*From Motor Learning to Interaction Learning in Robots*, Volume 264, Pages
193–207, 2010.

Michael A. Osborne, Roman Garnett, Stephen J. Roberts.

**Active Data Selection for
Sensor Networks with Faults and Changepoints**.

In *Proceedings of the 2010 24th IEEE International Conference on Advanced
Information Networking and Applications*, Pages 533–540, Washington, DC,
USA, 2010.

Veli Peltola, Antti Honkela.

**Variational
inference and learning for non-linear state-space models with state-dependent
observation noise**.

In *Proceedings of the 2010 IEEE International Workshop on Machine Learning
for Signal Processing, MLSP 2010*, Pages 190–195, 2010.

Dejan Petelin, Juš Kocijan.

**Application
of on-line Gaussian process models for pressure signal**.

In *Proceedings of the 11th International PhD Workshop on Systems and
Control, September 1-3, 2010, Veszprém, Hungary : a young generation viewpoint*,
Pages 39–44, 2010.

Gianluigi Pillonetto, Giuseppe De Nicolao.

**A new
kernel-based approach for linear system identification**.

*Automatica*, Volume 46, Pages 81–93, 2010.

Steven Reece, Stephen J. Roberts.

**An
Introduction to Gaussian Processes for the Kalman Filter Expert**.

In *Proceedings of the 13th International Conference on Information Fusion*,
Edinburgh, 2010.

Marisa Resende, Paulo J. Santos.

**Short - Term Load
Forecasting Using a Gaussian Process Model - Optimal Endogenous Regressor**.

Technical Report ISSN: 1645-2631, Issue 10, Instituto de Engenharia de Sistemas
e Computadores de Coimbra Institute of Systems Engineering and Computers INESC,
Coimbra, 2010.

Axel Rottmann, Wolfram Burgard.

**Learning
non-stationary system dynamics online using Gaussian processes**.

*Pattern Recognition*, *Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics)*, 2010.

Yunus Saatçi, Ryan Turner, Carl Edward Rasmussen.

**Gaussian Process Change
Point Models**.

In *Proceedings of the 27th International Conference on Machine Learning*,
Haifa, Israel, 2010.

Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor,
Remi Barillec.

**A Comparison
of Variational and Markov Chain Monte Carlo Methods for Inference in Partially
Observed Stochastic Dynamic Systems**.

*Journal of Signal Processing Systems*, Volume 61, Pages 51–59, 2010.

Q. Q. Shen, Z. H. Sun.

**Online Learning
Algorithm of Gaussian Process Based on Adaptive Natural Gradient for Regression**.

*Advanced Materials Research: Manufacturing Engineering and Automation*, Volume
1847, Pages 139–141, 2010.

Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen.

**State-space
inference and learning with Gaussian processes**.

In *Proceedings of 13th International Conference on Artificial Intelligence
and Statistics*, Volume 9, Pages 868–875, Sardinia, Italy, 2010.

Yafeng Yin, Hong Man, Jing Wang, Guang Yang.

**Human
motion change detection by hierarchical Gaussian process dynamical model with
particle filter**.

In *Proceedings of IEEE International Conference on Advanced Video and Signal
Based Surveillance, AVSS 2010*, Pages 307–314, 2010.

**2009**

Ali Abusnina, Daniel Kudenko.

**Adaptive Soft Sensor based on Moving Gaussian Process Window**.

*International Conference on Mechatronics Automation (ICMTMA)*, 2009.

Mauricio A. Álvarez, David Luengo, Neil D. Lawrence.

**Latent
Force Models**.

*Journal of Machine Learning Research - Proceedings Track*, Volume 5,
Pages 9–16, 2009.

Kristjan Ažman, Juš Kocijan.

**Fixed-structure
Gaussian process model**.

*International Journal of Systems Science*, Volume 40, Pages 1253–1262,
2009.

Jixu Chen, Minyoung Kim, Yu Wang, Qiang Ji.

**Switching
Gaussian Process Dynamic Models for simultaneous composite motion tracking and
recognition**.

In *Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition Workshops*, Pages 2655–2662, 2009.

S. Conti, J. P. Gosling, J. E. Oakley, A. O'Hagan.

**Gaussian
process emulation of dynamic computer codes**.

*Biometrika*, Volume 3, Pages 663–676, 2009.

Patrick Dallaire, Camille Besse, Brahim Chaib-Draa.

**Learning
Gaussian process models from uncertain data**.

*Neural Information Processing*,

Patrick Dallaire, Camille Besse, Stéphane Ross, Brahim Chaib-Draa.

**Bayesian
reinforcement learning in continuous POMDPs with Gaussian processes**.

In *IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
2009*, Pages 2604–2609, 2009.

Marc Peter Deisenroth, Marco F. Huber, Uwe D. Hannebeck.

**Analytic
moment-based Gaussian process filtering**.

In *Proceedings of the 26th Annual International Conference on Machine
Learning*, Pages 225–232, Montreal, Canada, 2009.

Marc Peter Deisenroth, Carl Edward Rasmussen.

**Bayesian Inference
for Efficient Learning in Control**.

In *Proceedings of Multidisciplinary Symposium on Reinforcement Learning
(MSRL)*, Montreal, Canada, 2009.

Marc Peter Deisenroth, Carl Edward Rasmussen.

**Bayesian
Inference for Efficient Learning in Control**.

In *Proceedings of the 10th International PhD Workshop on Systems and
Control, a Young Generation Viewpoint*, Hluboka nad Vltavou, Czech Republic,
2009.

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters.

**Gaussian process
dynamic programming**.

*Neurocomputing*, Volume 72, Pages 1508–1524, 2009.

Gregor Gregorčič, Gordon Lightbody.

**Gaussian process
approach for modelling of nonlinear systems**.

*Engineering Applications of Artificial Intelligence*, Volume 22, Pages
522–533, 2009.

Leslie Ikemoto, Okan Arikan, David A. Forsythe.

**Generalizing
motion edits with Gaussian processes**.

*ACM Transactions on Graphics*, Volume 28, Pages 1–12, 2009.

Johnsen Kho, Alex Rogers, Nicholas R. Jennings.

**Decentralised Control of
Adaptive Sampling in Wireless Sensor Networks**.

*ACM Transactions on Sensor Networks*, Volume 5, Pages 1–35, 2009.

Jonathan Ko, Dieter Fox.

**GP-BayesFilters:
Bayesian filtering using Gaussian process prediction and observation models**.

*Autonomous Robots*, Volume 27, Pages 75–90, 2009.

Jonathan Ko, Dieter Fox.

**Learning
GP-BayesFilters via Gaussian process latent variable models**.

*Robotics: science and systems*, 2009.

Juš Kocijan, Kristjan Ažman.

**Application
of varying parameters modelling with Gaussian processes**.

In *2nd IFAC International Conference on Intelligent Control Systems and
Signal Processing (ICONS)*, Istanbul, Turkey, 2009.

Subhasish Mohanty, Santanu Das, Aditi Chattopadhyay, Pedro Peralta.

**Gaussian
Process Time Series Model for Life Prognosis of Metallic Structures**.

*Journal of Intelligent Material Systems and Structures*, Volume 20, Pages
887–896, 2009.

Duy Nguyen-Tuong, Matthias Seeger, Jan Peters.

**Model
learning with local Gaussian process regression**.

*Advanced Robotics*, Volume 23, Pages 2015–2034, 2009.

Jerome Le Ny, George J. Pappas.

**On
trajectory optimization for active sensing in Gaussian process models**.

In *Proceedings of the IEEE Conference on Decision and Control*, Pages
6286–6292, 2009.

Gianluigi Pillonetto, Alessandro Chiuso.

**A Bayesian learning
approach to linear system identification with missing data**.

In *Proceedings of the IEEE Conference on Decision and Control*, Pages
4698–4703, 2009.

Axel Rottmann, Wolfram Burgard.

**Adaptive
autonomous control using online value iteration with Gaussian processes**.

In *Proceedings - IEEE International Conference on Robotics and Automation*,
Pages 2106–2111, 2009.

Ryan Turner, Marc Peter Deisenroth, Carl Edward Rasmussen.

**System
Identification in Gaussian Process Dynamical Systems**.

In *Nonparametric Bayes Workshop at NIPS*, Whistler, Canada, 2009.

Dit-Yan Yeung, Yu Zhang.

**Learning
Inverse Dynamics by Gaussian process Regression under the Multi-Task Learning
Framework**.

*The Path to Autonomous Robots*, 2009.

Guanling Zhou, Nanping Dong, Yuping Wang.

**Non-Linear
Dynamic Texture Analysis and Synthesis Using Constrained Gaussian Process
Latent Variable Model**.

In *Proceedings of the Pacific-Asia Conference on Circuits, Communications
and System (PACCS)*, Pages 27–30, 2009.

Wen-yun Zhou, Qaun Liu.

**A
Gaussian Processes Reinforcement Learning Method in Large Discrete State Spaces**.

In *Proceedings of International Conference on Advanced Computer Control
(ICACC)*, Pages 589–593, 2009.

**2008**

Kristjan Ažman, Juš Kocijan.

**Non-linear
model predictive control for models with local information and uncertainties**.

*Transactions of the Institute of Measurement and Control*, Volume 30,
2008.

B. Calderhead, M. Girolami, N. D. Lawrence.

**Accelerating
Bayesian Inference over Nonlinear Differential Equations with Gaussian
Processes**.

In *Advances in Neural Information Processing Systems 21 - Proceedings of the
2008 Conference*, Volume 1, Pages 217–224, 2008.

Kian M. Chai, Christopher Williams, Stefan Klanke, Sethu Vijayakumar.

**Multi-task
Gaussian Process Learning of Robot Inverse Dynamics**.

*Advances in Neural Information Processing Systems 21*, 2008.

Alessandro Chiuso, Gianluigi Pillonetto, Giuseppe De Nicolao.

**Subspace
identification using predictor estimation via Gaussian regression**.

In *Proceedings of the IEEE Conference on Decision and Control*, 2008.

Jongeun Choi, Joonho Lee, Songhwai Oh.

**Swarm
intelligence for achieving the global maximum using spatio-temporal Gaussian
processes**.

In *Proceedings of American Control Conference (ACC)*, Pages 135–140,
Seattle, WA, 2008.

Jongeun Choi, Joonho Lee, Songhwai Oh.

**Biologically-inspired
navigation strategies for swarm intelligence using spatial Gaussian processes**.

In *Proceedings of IFAC 17th World Congress*, Pages 593–598, Seoul, South
Korea, 2008.

Giuseppe De Nicolao, Gianluigi Pillonetto.

**A
new kernel-based approach for system identification**.

In *Proceedings of American Control Conference (ACC)*, Pages 4510–4516,
Seattle, WA, 2008.

Marc Peter Deisenroth, Jan Peters, Carl Edward Rasmussen.

**Approximate
dynamic programming with Gaussian processes**.

In *Proceedings of American Control Conference (ACC)*, Pages 4480–4485,
Seattle, WA, 2008.

Marc Peter Deisenroth, Carl Edward Rasmussen, Jan Peters.

**Model-Based
Reinforcement Learning with Continuous States and Actions**.

In *Proceedings of the European Symposium on Artificial Neural Networks
(ESANN)*, Pages 19–24, Bruges, Belgium, 2008.

Carl Henrik Ek, Philip H.S. Torr, Neil D. Lawrence.

**Gaussian Process
Latent Variable Models for Human Pose Estimation**.

*Machine Learning for Multimodal Interaction*, *Lecture Notes in
Computer Science*, 2008.

Alexandra Grancharova, Juš Kocijan, Tor Arne Johansen.

**Explicit
stochastic predictive control of combustion plants based on Gaussian process
models**.

*Automatica*, Volume 44, Pages 1621–1631, 2008.

Gregor Gregorčič, Gordon Lightbody.

**Nonlinear system
identification: From multiple-model networks to Gaussian processes**.

*Engineering Applications of Artificial Intelligence*, Volume 21, Pages
1035–1055, 2008.

Tomohiro Hachino, Hitoshi Takata.

**Identification
of continuous-time nonlinear systems by using a Gaussian process model**.

*IEEJ Transactions on Electrical and Electronic Engineering*, Volume 3,
Pages 620–628, 2008.

Jonathan Ko, Dieter Fox.

**GP-Bayes
Filters: Bayesian filtering using Gaussian process prediction and observation
models**.

In *Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)*, Pages 3471–3476, Nice, France, 2008.

Juš Kocijan.

**Gaussian Process Models for Systems
Identification**.

In *Proceedings of 9th Intetnational PhD Workshop on Systems and Control:
young generation viewpoint*, Izola, Slovenia, 2008.

Juš Kocijan, Kristjan Ažman.

**Gaussian
process model identification: a process engineering case study**.

*Systems Science Journal*, Volume 34, Pages 31–38, 2008.

Juš Kocijan, Bojan Likar.

**Gas-liquid
separator modelling and simulation with Gaussian-process models**.

*Simulation Modelling Practice and Theory*, Volume 16, Pages 910–922,
2008.

Duy Nguyen-Tuong, Jan Peters.

**Learning
Robot Dynamics for Computed Torque Control Using Local Gaussian Processes
Regression**.

In *Symposium on Learning and Adaptive Behaviors for Robotic Systems*,
Pages 59–64, 2008.

Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Bernhard Schölkopf.

**Learning
Inverse Dynamics: a Comparison**.

In *Proceedings of the European Symposium on Artificial Neural Networks
(ESANN)*, Pages 13–18, Bruges, Belgium, 2008.

Duy Nguyen-Tuong, Matthias Seeger, Jan Peters.

**Computed
Torque Control with Nonparametric Regression Models**.

In *Proceedings of the American Control Conference (ACC)*, Pages 212–217,
2008.

Gianluigi Pillonetto, Alessandro Chiuso, Giuseppe De Nicolao.

**Predictor
estimation via Gaussian regression**.

In *Proceedings of the IEEE Conference on Decision and Control*, 2008.

Luc Pronzato.

**Optimal
experimental design and some related control problems**.

*Automatica*, Volume 44, Pages 303–325, 2008.

Carl Edward Rasmussen, Marc Peter Deisenroth.

**Probabilistic
Inference for Fast Learning in Control**.

*Recent Advances in Reinforcement Learning, Lecture Notes on Computer Science*,
2008.

Kyle Schmitt, Justin Madsen, Mihai Anitescu, Dan Negrut.

**A
Gaussian process based approach for handling uncertainty in vehicle dynamics
simulation**.

*International Mechanical Engineering Congress and Exposition (IMECE)*,
2008.

Fernando di Sciascio, Adriana N. Amicarelli.

**Biomass
estimation in batch biotechnological processes by Bayesian Gaussian process
regression**.

*Computers and Chemical Engineering*, Volume 32, Pages 3264–3273, 2008.

Sylvain Vinet, Emmanuel Vazquez.

**Black-box
identification and simulation of continuous-time nonlinear systems with random
processes**.

In *Proceedings of the IFAC 17th World Congress*, Pages 14391–14396,
Seoul, South Korea, 2008.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.

**Gaussian
Process Dynamical Models for Human Motion**.

*IEEE transactions on pattern analysis and machine intelligence*, Volume
30, Pages 283–298, 2008.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.

**Erratum:
Gaussian process dynamical models for human motion**.

*IEEE transactions on pattern analysis and machine intelligence*, Volume
30, Pages 1118, 2008.

Jin Yuan, Kesheng Wang, Tao Yu, Minglun Fang.

**Reliable
multi-objective optimization of high-speed WEDM process based on Gaussian
process regression**.

*International Journal of Machine Tools and Manufacture*, Volume 48, Pages
47–60, 2008.

**2007**

Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor.

**Gaussian
Process Approximations of Stochastic Differential Equations**.

In *Journal of Machine Learning Research: Workshop and Conference Proceedings*,
Volume 1, Pages 1–16, 2007.

Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor.

**Variational
Inference for Diffusion Processes**.

*Advances in Neural Information Processing Systems*, 2007.

Kristjan Ažman, Juš Kocijan.

**Application of
Gaussian processes for black-box modelling of biosystems**.

*ISA transactions*, Volume 46, Pages 443–457, 2007.

S. Calinon, F. Guenter, A Billard.

**On Learning,
Representing, and Generalizing a Task in a Humanoid Robot**.

*Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on*,
Volume 37, Pages 286–298, 2007.

Marc P. Deisenroth, Florian Weissel, Toshiyuki Ohtsuka, Uwe D. Hanebeck.

**Online-Computation
Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous
State and Control Spaces**.

In *In Proceedings of the European Control Conference (ECC 2007*, 2007.

Luka Eciolaza, M. Alkarouri, N. D. Lawrence, V. Kadirkamanathan, P. J. Fleming.

**Gaussian
Process Latent Variable Models for Fault Detection**.

In *Proceedings of the 2007 IEEE Symposium on Computational Intelligence and
Data Mining (CIDM 2007)*, Pages 287–292, Honolulu, HI, 2007.

Stephen Faul, Gregor Gregorčič, Geraldine Boylan, William Marnane, Gordon
Lightbody, Sean Connolly.

**Gaussian
process modeling of EEG for the detection of neonatal seizures**.

*IEEE Transactions on Biomedical Engineering*, Volume 54, Pages 2151–2162,
2007.

Alexandra Grancharova, Juš Kocijan.

**Stochastic predictive control of
a thermoelectric power plant**.

In *Proceedings of the International Conference Automatics and Informatics'07*,
Pages I-13–I-16, Sofia, 2007.

Alexandra Grancharova, Juš Kocijan, Tor Arne Johansen.

**Explicit
stochastic nonlinear predictive control based on Gaussian process models**.

In *Proceedings of European control conference (ECC)*, Pages 2340–2347,
Kos, Greece, 2007.

Gregor Gregorčič, Gordon Lightbody.

**Local
model identification with Gaussian processes**.

*IEEE Transactions on neural networks*, Volume 18, Pages 1404–1423, 2007.

Tomohiro Hachino, Visakan Kadirkamanathan.

**Time
series forecasting using multiple Gaussian process prior model**.

In *IEEE Symposium on Computational Intelligence and Data Mining (CIDM)*,
Pages 604–609, 2007.

Jonathan Ko, Daniel J. Klein, Dieter Fox, Dirk Haehnel.

**Gaussian
Processes and Reinforcement Learning for Identification and Control of an
Autonomous Blimp**.

In *Proceedings of the International Conference on Robotics and Automation*,
Pages 742–747, Rome, Italy, 2007.

Jonathan Ko, Daniel J. Klein, Dieter Fox, Dirk Haehnel.

**GP-UKF:
Unscented Kalman filters with Gaussian process prediction and observation models**.

In *Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent
Robots and Systems*, Pages 1901–1907, San Diego, CA, 2007.

Juš Kocijan.

**Identifikacija
nelinearnih sistemov z Gaussovimi procesi**.

*Modeliranje dinamičnih sistemov z umetnimi nevronskimi mrežami in sorodnimi
metodami*, Pages 73–86, 2007, (in Slovene).

Juš Kocijan, Kristjan Ažman.

**Gaussian process model identification: a process engineering case study**.

In *Proceedings of the 16th International Conference on Systems Science*,
Volume 1, Pages 418–427, Wroclaw, Poland, 2007.

Juš Kocijan, Kristjan Ažman, Alexandra Grancharova.

**The concept
for Gaussian process model based system identification toolbox**.

In *Proceedings of the InternationalConference on Computer Systems and
Technologies (CompSysTech)*, Pages IIIA.23–1—-IIIA.23–6, Rousse, Bulgaria,
2007.

Juš Kocijan, Bojan Likar.

**Gas-liquid separator modelling and simulation with Gaussian-process models**.

In *Proceedings of the 6th EUROSIM Congress on Modelling and Simulation
(EUROSIM)*, Ljubljana, Slovenia, 2007.

William E. Leithead, Yunong Zhang.

**O(N-2)-operation
approximation of covariance matrix inverse in Gaussian process regression based
on quasi-Newton BFGS method**.

*Communications in Statistics-Simulation and Computation*, Volume 36,
Pages 367–380, 2007.

Bojan Likar, Juš Kocijan.

**Predictive
control of a gas-liquid separation plant based on a Gaussian process model**.

*Computers and chemical engineering*, Volume 31, Pages 142–152, 2007.

Marta Neve, Giuseppe De Nicolao, Laura Marchesi.

**Nonparametric
identification of population models via Gaussian processes**.

*Automatica*, Volume 43, Pages 1134–1144, 2007.

Rainer Palm.

**Multiple-step-ahead
prediction in control systems with Gaussian process models and TS-fuzzy models**.

*Engineering Applications of Artificial Intelligence*, Volume 20, Pages
1023–1035, 2007.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.

**Multifactor
Gaussian process models for style-content separation**.

In *Proceedings of the 24th international conference on Machine learning*,
Oregon, 2007.

H. Wu, F. Sun.

**Adaptive
Kriging control of discrete-time nonlinear systems**.

*IET Control Theory & Applications*, Volume 1, Pages 646–656, 2007.

Yunong Zhang, William E. Leithead.

**Approximate
implementation of the logarithm of the matrix determinant in Gaussian process
regression**.

*Journal of Statistical Computation and Simulation*, Volume 77, Pages
329–348, 2007.

**2006**

Kristjan Ažman, Juš Kocijan.

**Gaussian process model validation: biotechnological case studies**.

In *Proceedings of the 5th Vienna Symposium on Mathematical Modeling
(MathMod)*, Vienna, Austria, 2006.

Kristjan Ažman, Juš Kocijan.

**Identifikacija
dinamičnega sistema z znanim modelom šuma z modelom na osnovi Gaussovih
procesov**.

In *Zbornik petnajste elektrotehniške in računalniške konference (ERK)*,
Pages 289–292, Portorož, Slovenia, 2006.

Kristjan Ažman, Juš Kocijan.

**An
application of Gaussian process models for control design**.

In *UKACC International Control Conference*, Glasgow, UK, 2006.

Boštjan Grašič, Primož Mlakar, Marija Zlata Božnar.

**Ozone
prediction based on neural networks and Gaussian processes**.

*Nuovo Cimento della Societa Italiana di Fisica, Sect. C*, Volume 29,
Pages 651–662, 2006.

David B. Grimes, Rawichote Chalodhorn, Rajesh P. N. Rao.

**Dynamic
Imitation in a Humanoid Robot through Nonparametric Probabilistic Inference**.

In *Robotics: Science and Systems*, 2006.

Đani Juričić, Juš Kocijan.

**Fault detection based on Gaussian process model**.

In *Proceedings of the 5th Vienna Symposium on Mathematical Modeling
(MathMod)*, Vienna, Austria, 2006.

Douglas J. Leith, Roderick Murray-Smith, William E. Leithead.

**Inference of
disjoint linear and nonlinear subdomains of a nonlinear mapping**.

*Automatica*, Volume 42, Pages 849–858, 2006.

Kooksang Moon, Vladimir Pavlović.

**Impact
of Dynamics on Subspace Embedding and Tracking of Sequences**.

In *Proceedings - 2006 IEEE Computer Society Conference on Computer Vision
and Pattern Recognition (CVPR)*, Volume 1, Pages 198–205, 2006.

Keith Kian Seng Neo, William E. Leithead, Yunong Zhang.

**Multi-frequency
scale Gaussian regression for noisy time-series data**.

In *UKACC International Control Conference*, Glasgow, UK, 2006.

Masatarou Ohmi, Hiroyuki Mori.

**A
Gaussian processes technique for short-term load forecasting with
considerations of uncertainty**.

*IEEJ Transactions on Power and Energy*, Volume 126, Pages 202–208, 2006.

Carl Edward Rasmussen, Christopher K. I. Williams.

**Gaussian Processes for
Machine Learning**.

MIT Press, 2006.

Keith Russell Thompson, David James Murray-Smith.

**Implementation of Gaussian process models for nonlinear system
identification**.

In *Proceedings of the 5th Vienna Symposium on Mathematical Modeling
(MathMod)*, Vienna, Austria, 2006.

Raquel Urtasun, David J. Fleet, Pascal Fua.

**3D
people tracking with Gaussian process dynamical models**.

In *Proceedings - 2006 IEEE Computer Society Conference on Computer Vision
and Pattern Recognition (CVPR)*, Volume 1, Pages 238–245, 2006.

**2005**

Kristjan Ažman.

**Incorporating
prior knowledge into Gaussian process model**.

In *Proceedings of 6th International PhD Workshop on Systems and Control - A
Young Generation Viewpoint*, Volume A, Pages 253–256, Izola, Slovenia, 2005.

Kristjan Ažman, Juš Kocijan.

**An example
of Gaussian process model identification**.

In *Proceedings of 28th International conference MIPRO, CIS - Inteligent
Systems*, Pages 79–84, Opatija, Croatia, 2005.

Kristjan Ažman, Juš Kocijan.

**Identifikacija
dinamičnega sistema s histerezo z modelom na osnovi Gaussovih procesov**.

In *Zbornik štirinajste elektrotehniške in računalniške konference (ERK 2005)*,
Volume A, Pages 253–256, Portorož, Slovenia, 2005.

Kristjan Ažman, Juš Kocijan.

**Comprising
prior knowledge in dynamic Gaussian process models**.

In *Proceedings of the International Conference on Computer Systems and
Technologies (CompSysTech)*, Pages IIIB.2–1—-IIIB.2–6, Varna, Bulgaria,
2005.

Yaakov Engel, Shie Mannor, Ron Meir.

**Reinforcement
learning with Gaussian processes**.

In *Proceedings of the 22nd international conference on Machine learning*,
Pages 201–208, Bonn, Germany, 2005.

Yaakov Engel, Peter Szabo, Dmitry Volkinshtein.

**Learning
to Control an Octopus Arm with Gaussian Process Temporal Difference Methods**.

In *Advances in Neural Information Processing Systems*, Volume 18, Pages
347–354, 2005.

Agathe Girard, Roderick Murray-Smith.

**Gaussian
processes: Prediction at a noisy input and application to iterative
multiple-step ahead forecasting of time-series**.

*Lecture Notes in Computer Science*, 2005.

Gregor Gregorčič, Gordon Lightbody.

**Gaussian
Process Approaches to Nonlinear Modelling for Control**.

*Intelligent Control Systems Using Computational Intelligence Techniques*,
*IEE Intelligent Control Series*, 2005.

Jostein Hansen, Roderick Murray-Smith, Tor Arne Johansen.

**Nonparametric
identification of linearizations and uncertainty using Gaussian process
models—application to robust wheel slip control**.

In *Joint 44th IEEE conference on decision and control and European control
conference (CDC-ECC)*, Pages 7994–7999, Sevilla, Spain, 2005.

Juš Kocijan, Agathe Girard.

**Incorporating
linear local models in Gaussian process model**.

In *Proceedings of IFAC 16th World Congress*, Prague, Czech Republic,
2005.

Juš Kocijan, Agathe Girard, Blaž Banko, Roderick Murray-Smith.

**Dynamic
systems identification with Gaussian processes**.

*Mathematical and Computer Modelling of Dynamical Systems*, Volume 11,
Pages 411–424, 2005.

Juš Kocijan, Roderick Murray-Smith.

**Nonlinear predictive control with
Gaussian process model**.

*Lecture Notes in Computer Science*, 2005.

William E. Leithead.

**Identification
of Nonlinear Dynamic Systems by Combining Equilibrium and Off-Equilibrium
Information**.

In *Proceedings of International Conference on Industrial Electronics and
Control Applications (ICIECA)*, Quito, Ecuador, 2005.

William E. Leithead, Keith Kian Seng Neo, Douglas J. Leith.

**Gaussian
regression based on models with two stochastic processes**.

In *Proceedings of IFAC 16th World Congress, Prague, Czech Republic*,
2005.

William E. Leithead, Yunong Zhang, Douglas J. Leith.

**Efficient
hyperparameter estimation of Gaussian process regression based on quasi-Newton
BFGS update and power series approximation**.

In *Proceedings of IFAC 16th World Congress*, Prague, Czech Republic,
2005.

William E. Leithead, Yunong Zhang, Douglas J. Leith.

**Time-series
Gaussian process regression based on Toeplitz computation of O(N2) operations
and O(N) level storage**.

In *Joint 44th IEEE conference on decision and control and European control
conference (CDC-ECC)*, Sevilla, Spain, 2005.

William E. Leithead, Yunong Zhang, Keith Kian Seng Neo.

**Wind
turbine rotor acceleration: identification using Gaussian regression**.

In *Proceedings of International conference on informatics in control
automation and robotics (ICINCO)*, Barcelona, Spain, 2005.

Hiroyuki Mori, Masatarou Ohmi.

**Probabilistic
short-term load forecasting with Gaussian processes**.

In *Proceedings of the 13th International Conference on Intelligent Systems
Application to Power Systems*, Pages 452–457, Arlington, VA, 2005.

Roderick Murray-Smith, Barak A. Pearlmutter.

**Transformations
of Gaussian process priors**.

*Lecture Notes in Artificial Intelligence*, 2005.

Rainer Palm.

**Multi-step-ahead
prediction with Gaussian Processes and TS-Fuzzy Models**.

In *Proceedings of 14th IEEE International Conference on Fuzzy Systems*,
Pages 945–950, 2005.

Daniel Sbarbaro, Roderick Murray-Smith.

**An
adaptive nonparametric controller for a class of nonminimum phase non-linear
system**.

In *Proceedings of IFAC 16th World Congress*, Prague, Czech Republic,
2005.

Daniel Sbarbaro, Roderick Murray-Smith.

**Self-tuning control of nonlinear
systems using Gaussian process prior models**.

*Lecture Notes in Computer Science*, 2005.

Jian Qing Shi, Roderick Murray-Smith, D. Mike Titterington.

**Hierarchical Gaussian process
mixtures for regression**.

*Statistics and Computing*, Volume 15, Pages 31–41, 2005.

Sethu Vijayakumar, Aaron D'souza, Stefan Schaal.

**Incremental
Online Learning in High Dimensions**.

*Neural Computation*, Volume 17, Pages 2602–2634, 2005.

Jack M. Wang, David J. Fleet, Aaron Hertzmann.

**Gaussian
Process Dynamical Models**.

*Advances in Neural Information Processing Systems*, Volume 18, Pages
1441–1448, 2005.

Zhi-hua Xiong, Hai-bin Yang, Yun-feng Wu, Hui-he Shao.

**Sparse
GP-based soft sensor applied to the power plant**.

*Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng.)*, Volume
25, Pages 130–133, 2005.

Zhi-hua Xiong, Wei-qing Zhang, Yu Zhao, Hui-he Shao.

**Thermal parameter soft
sensor based on the mixture of Gaussian processes**.

*Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc. Electr. Eng.)*, Volume
25, Pages 30–33, 2005.

Yunong Zhang, William E. Leithead.

**Exploiting Hessian
matrix and trust region algorithm in hyperparameters estimation of Gaussian
process**.

*Applied Mathematics and Computation*, Volume 171, Pages 1264–1281, 2005.

**2004**

Juš Kocijan, Douglas J. Leith.

**Derivative
Observations Used in Predictive Control**.

In *Proceedings of IEEE Melecon*, Volume 1, Pages 379–382, Dubrovnik, Croatia,
2004.

Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, Agathe Girard.

**Gaussian Process Model Based
Predictive Control**.

In *Proceedings of 4th American Control Conference (ACC 2004)*, Pages 2214–2218,
Boston, MA, 2004.

Douglas J. Leith, Martin Heidl, John V. Ringwood.

**Gaussian Process
Prior Models for Electrical Load Forecasting**.

In *International Conference on Probabilistic Methods Applied to Power
Systems*, Pages 112–117, 2004.

Carl Edward Rasmussen, Malte Kuss.

**Gaussian
Processes in Reinforcement Learning**.

In *Advances in Neural Information Processing Systems conference*, Volume
16, Pages 751–759, 2004.

Daniel Sbarbaro, Roderick Murray-Smith, Arturo Valdes.

**Multivariable
generalized minimum variance control based on artificial neural networks and
Gaussian process models**.

In *International Symposium on Neural Networks*, 2004.

**2003**

Agathe Girard, Carl Edward Rasmussen, Joaquin Quiñonero-Candela, Roderick
Murray-Smith.

**Bayesian
regression and Gaussian process priors with uncertain inputs - application to
multiple-step ahead time series forecasting**.

In *Advances in Neural Information Processing Systems conference*, Volume
15, Pages 529–536, 2003.

Gary Gray, Roderick Murray-Smith, Keith Russell Thompson, David James
Murray-Smith.

**Tutorial
example of Gaussian process prior modelling applied to twin-tank system**.

Technical Report DCS TR-2003-151, University of Glasgow, Glasgow, 2003.

Gregor Gregorčič, Gordon Lightbody.

**Internal
model control based on Gaussian process prior model**.

In *Proceedings of the 2003 American Control Conference (ACC 2003)*, Pages
4981–4986, Denver, CO, 2003.

Gregor Gregorčič, Gordon Lightbody.

**From multiple model networks to the Gaussian processes prior model**.

In *Proceedings of IFAC ICONS conference*, Pages 149–154, Faro, Portugal,
2003.

Gregor Gregorčič, Gordon Lightbody.

**An
affine Gaussian process approach for nonlinear system identification**.

*Systems Science Journal*, Volume 29, Pages 47–63, 2003.

Jostein Hansen.

**Using Gaussian processes as a modelling tool in control systems**.

Technical Report DCS TR-2003, University of Glasgow, Glasgow, 2003.

Juš Kocijan, Blaž Banko, Bojan Likar, Agathe Girard, Roderick Murray-Smith,
Carl Edward Rasmussen.

**A
case based comparison of identification with neural network and Gaussian
process models**.

In *Proceedings of IFAC ICONS Conference*, Volume 1, Pages 137–142, Faro,
Portugal, 2003.

Juš Kocijan, Agathe Girard, Blaž Banko, Roderick Murray-Smith.

**Dynamic
systems identification with Gaussian processes**.

In *Proceedings of 4th IMACS Symposium on Mathematical Modelling (MathMod)*,
Pages 776–784, Vienna, Austria, 2003.

Juš Kocijan, Agathe Girard, Douglas J. Leith.

**Incorporating
linear local models in Gaussian process model**.

Technical Report DP-8895, Institut Jožef Stefan, Ljubljana, 2003.

Juš Kocijan, Roderick Murray-Smith, Carl Edward Rasmussen, Bojan Likar.

**Predictive
control with Gaussian process models**.

In *The IEEE Region 8 EUROCON: computer as a tool*, Volume A, Pages
352–356, Ljubljana, Slovenia, 2003.

Douglas J. Leith, William E. Leithead, Roderick Murray-Smith.

**Nonlinear
structure identification with application to Wiener-Hammerstein systems**.

In *Proceedings of 13th IFAC Symposium on System Identification*,
Rotterdam, Netherlands, 2003.

William E. Leithead, Ercan Solak, Douglas J. Leith.

**Direct
identification of nonlinear structure using Gaussian process prior models**.

In *Proceedings of European Control Conference (ECC 2003), Cambridge, UK*,
Cambridge, UK, 2003.

Roderick Murray-Smith, Daniel Sbarbaro, Carl Edward Rasmussen, Agathe Girard.

**Adaptive,
cautious, predictive control with Gaussian process priors**.

In *Proceedings of 13th IFAC Symposium on System Identification*,
Rotterdam, Netherlands, 2003.

Joaquin Quiñonero-Candela, Agathe Girard.

**Prediction
at uncertain input for Gaussian processes and relevance vector machines -
Application to multiple-step ahead time-series forecasting**.

Technical Report IMM-2003-18, Technical University Denmark, Informatics and
Mathematical Modelling, Kongens Lyngby, 2003.

Joaquin Quiñonero-Candela, Agathe Girard, Jan Larsen, Carl Edward Rasmussen.

**Propagation
of uncertainty in Bayesian kernel models - Application to multiple-step ahead
forecasting**.

In *Proceedings of IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP)*, Volume 2, Pages 701–704, 2003.

Daniel Sbarbaro, Roderick Murray-Smith.

**Self-tuning
control of nonlinear systems using Gaussian process prior models**.

Technical Report DCS TR-2003-143, University of Glasgow, Glasgow, 2003.

Ercan Solak, Roderick Murray-Smith, William E. Leithead, Douglas J. Leith, Carl
Edward Rasmussen.

**Derivative
observations in Gaussian process models of dynamic systems**.

In *Advances in Neural Information Processing Systems conference*, Volume
15, Pages 529–536, 2003.

**2002**

Blaž Banko, Juš Kocijan.

**Uporaba
Gaussovih procesov za identifikacijo nelinearnih sistemov**.

In *Zbornik enajste elektrotehniške in računalniške konference (ERK 2002)*,
Volume A, Pages 323–326, Portorož, Slovenia, 2002.

Agathe Girard, Carl Edward Rasmussen, Roderick Murray-Smith.

**Gaussian
process priors with uncertain inputs: Multiple-step-ahead prediction**.

Technical Report DCS TR-2002-119, University of Glasgow, Glasgow, 2002.

Gregor Gregorčič, Gordon Lightbody.

**Gaussian
Processes for Modelling of Dynamic Non-linear Systems**.

In *Proceedings of the Irish Signals and Systems Conference*, Pages 141–147,
Cork, Ireland, 2002.

Gregor Gregorčič, Gordon Lightbody.

**Gaussian
Process for Internal Model Control**.

In *Proceedings of 3rd International PhD Workshop on Advances in Supervision
and Control Systems, A Young Generation Viewpoint*, Pages 39–46, Strunjan,
Slovenia, 2002.

Juš Kocijan.

**Gaussian
Process Model Based Predictive Control**.

Technical Report DP-8710, Institute Jožef Stefan, Ljubljana, 2002.

Juš Kocijan, Bojan Likar, Blaž Banko, Agathe Girard, Roderick Murray-Smith,
Carl Edward Rasmussen.

**Identification
of pH neutralization process with neural networks and Gaussian process model:
MAC project**.

Technical Report DP-8575, Institute Jožef Stefan, Ljubljana, 2002.

Douglas J. Leith.

**On identifying nonlinear dynamic structure from time series data**.

*Proceedings of Workshop on Modern Methods for Data Intensive Modelling*,
Maynooth, Ireland, 2002.

Douglas J. Leith, William E. Leithead, Ercan Solak, Roderick Murray-Smith.

**Divide and conquer identification using Gaussian processes**.

In *Proceedings of IEE Workshop on Nonlinear and Non-Gaussian signal
processing (N2SP)*, Peebles, UK, 2002.

Roderick Murray-Smith, Daniel Sbarbaro.

**Nonlinear adaptive control using
nonparametric Gaussian process prior models**.

In *Proceedings of IFAC 15th World Congress*, Barcelona, Spain, 2002.

Roderick Murray-Smith, Robert Shorten, Douglas J. Leith.

**Nonparametric models of dynamic systems**.

In *Proceedings of IEE Workshop on Nonlinear and Non-Gaussian signal
processing (N2SP)*, Peebles, UK, 2002.

**2001**

Vladan Babovic, Maarten Keijzer.

**A
Gaussian Process Model Applied to Prediction of the Water Levels in Venice
Lagoon**.

In *Proceedings Of The XXIX Congress Of International Association For
Hydraulic Research*, Pages 509–513, 2001.

Roderick Murray-Smith, Agathe Girard.

**Gaussian
Process priors with ARMA noise models**.

In *Irish Signals and Systems Conference, Maynooth, Ireland*, Pages
147–152, Maynooth, Ireland, 2001.

**2000**

William E. Leithead, Douglas J. Leith, Roderick Murray-Smith.

**A Gaussian Process
prior/velocity-based Framework for Nonlinear Modelling and Control**.

In *Irish Signals and Systems Conference*, Dublin, Ireland, 2000.

Douglas J. Leith, Roderick Murray-Smith, William E. Leithead.

**Nonlinear
structure identification: A non-parametric/velocity-based approach**.

In *Proceedings of the UKACC Control Conference*, Cambridge, UK, 2000.

**1999**

Roderick Murray-Smith, Tor Arne Johansen, Robert Shorten.

**On transient
dynamics, off-equilibrium behaviour and identification in blended multiple
model structures**.

In *Proceedings of the European Control Conference (ECC99)*, Pages BA–14,
Karlsruhe, Germany, 1999.

E-mail:jus[dot]kocijan[at]ijs[dot]si