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

12 June 2013

Fredrik Lindsten

Papers citing "Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC"

50 / 57 papers shown

Title
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Zhidi Lin Ying Li Feng Yin Juan Maroñas Alexandre Thiéry 54 0 0 24 Mar 2025
Recursive Gaussian Process State Space Model Tengjie Zheng Lin Cheng Shengping Gong Xu Huang 74 0 0 22 Nov 2024
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting Zhihao Yu Liantao Ma Yasha Wang Junfeng Zhao AI4TS 15 0 0 28 Dec 2023
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference Zhidi Lin Yiyong Sun Feng Yin Alexandre Thiéry 26 4 0 10 Dec 2023
A projected nonlinear state-space model for forecasting time series signals Christian Donner Anuj Mishra Hideaki Shimazaki AI4TS 16 0 0 22 Nov 2023
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models Alonso Marco Elias Morley Claire Tomlin 31 2 0 13 Sep 2023
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models Zhidi Lin Juan Maroñas Ying Li Feng Yin Sergios Theodoridis 24 3 0 03 Sep 2023
The Bayesian Context Trees State Space Model for time series modelling and forecasting I. Papageorgiou Ioannis Kontoyiannis AI4TS 17 2 0 02 Aug 2023
Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States Robert Lefringhausen Supitsana Srithasan Armin Lederer Sandra Hirche 20 6 0 31 Mar 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Deep networks for system identification: a Survey G. Pillonetto Aleksandr Aravkin Daniel Gedon L. Ljung Antônio H. Ribeiro Thomas B. Schon OOD 37 35 0 30 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 31 7 0 21 Jan 2023
Output-Dependent Gaussian Process State-Space Model Zhidi Lin Lei Cheng Feng Yin Le Xu Shuguang Cui UQCV 41 5 0 15 Dec 2022
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction Ning Wang AI4TS 17 0 0 08 May 2022
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes E. Bradford Lars Imsland M. Reble Ehecatl Antonio del Rio Chanona 11 11 0 14 Aug 2021
Active Learning in Gaussian Process State Space Model H. Yu Dingling Yao Christoph Zimmer Marc Toussaint D. Nguyen-Tuong GP 19 4 0 30 Jul 2021
State-space aerodynamic model reveals high force control authority and predictability in flapping flight Y. Bayiz Bo Cheng 6 11 0 14 Mar 2021
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective A. R. Geist Sebastian Trimpe AI4CE 11 17 0 11 Dec 2020
Stochastic embeddings of dynamical phenomena through variational autoencoders C. A. García P. Félix J. Presedo A. Otero BDL 14 2 0 13 Oct 2020
An Intuitive Tutorial to Gaussian Process Regression Jie Wang GP 9 74 0 22 Sep 2020
Prediction with Approximated Gaussian Process Dynamical Models Thomas Beckers Sandra Hirche AI4CE 6 18 0 25 Jun 2020
Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data Steven Atkinson 6 8 0 07 Jun 2020
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes A. R. Geist Sebastian Trimpe 17 21 0 23 Apr 2020
Considering discrepancy when calibrating a mechanistic electrophysiology model Chon Lok Lei Sanmitra Ghosh Dominic G. Whittaker Y. Aboelkassem K. Beattie ... R. W. dos Santos J. Walmsley Keith Worden Gary R. Mirams Richard D. Wilkinson 15 48 0 13 Jan 2020
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue Raed Al Kontar 101 3 0 15 Oct 2019
Structured Variational Inference in Unstable Gaussian Process State Space Models Silvan Melchior Sebastian Curi Felix Berkenkamp Andreas Krause 17 4 0 16 Jul 2019
The Use of Gaussian Processes in System Identification Simo Särkkä GP AI4TS 11 8 0 13 Jul 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 26 30 0 13 Jun 2019
Recursive Estimation for Sparse Gaussian Process Regression Manuel Schürch Dario Azzimonti A. Benavoli Marco Zaffalon 17 32 0 28 May 2019
A novel Multiplicative Polynomial Kernel for Volterra series identification Alberto Dalla Libera R. Carli G. Pillonetto 4 18 0 20 May 2019
Moment-Based Variational Inference for Markov Jump Processes C. Wildner Heinz Koeppl 17 10 0 14 May 2019
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler Duo Xu 18 2 0 03 Jan 2019
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations J. Hendriks Carl Jidling A. Wills Thomas B. Schon 11 9 0 18 Dec 2018
Non-Factorised Variational Inference in Dynamical Systems Alessandro Davide Ialongo Mark van der Wilk J. Hensman C. Rasmussen 17 6 0 14 Dec 2018
Continuous time Gaussian process dynamical models in gene regulatory network inference A. Aalto L. Viitasaari Pauliina Ilmonen Laurent Mombaerts Jorge M. Gonçalves 14 7 0 24 Aug 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 13 681 0 03 Jul 2018
Learning dynamical systems with particle stochastic approximation EM Andreas Svensson Fredrik Lindsten 27 9 0 25 Jun 2018
A Local Information Criterion for Dynamical Systems Arash Mehrjou Friedrich Solowjow Sebastian Trimpe Bernhard Schölkopf 9 3 0 27 May 2018
Specialized Interior Point Algorithm for Stable Nonlinear System Identification Jack Umenberger I. Manchester 15 32 0 02 Mar 2018
Probabilistic Recurrent State-Space Models Andreas Doerr Christian Daniel Martin Schiegg D. Nguyen-Tuong S. Schaal Marc Toussaint Sebastian Trimpe 13 121 0 31 Jan 2018
The Generalized Cross Validation Filter Giulio Bottegal G. Pillonetto 9 16 0 08 Jun 2017
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis Tom Nicholson M. Deisenroth J. Hensman 24 111 0 30 May 2017
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks Guokun Lai Wei-Cheng Chang Yiming Yang Hanxiao Liu BDL AI4TS 40 1,950 0 21 Mar 2017
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo Thomas B. Schon Andreas Svensson Lawrence M. Murray Fredrik Lindsten 17 41 0 07 Mar 2017
An Efficient, Expressive and Local Minima-free Method for Learning Controlled Dynamical Systems Ahmed S. Hefny Carlton Downey Geoffrey J. Gordon 11 6 0 12 Feb 2017
The interplay between system identification and machine learning G. Pillonetto 26 4 0 29 Dec 2016
Recurrent switching linear dynamical systems Scott W. Linderman Andrew C. Miller Ryan P. Adams David M. Blei Liam Paninski Matthew J. Johnson 36 69 0 26 Oct 2016
A flexible state space model for learning nonlinear dynamical systems Andreas Svensson Thomas B. Schon 14 104 0 17 Mar 2016
System Identification through Online Sparse Gaussian Process Regression with Input Noise Hildo Bijl Thomas B. Schon J. Wingerden M. Verhaegen 39 41 0 29 Jan 2016
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models Andreas Svensson Arno Solin Simo Särkkä Thomas B. Schon 19 57 0 07 Jun 2015