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1306.2861
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Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
12 June 2013
R. Frigola
Fredrik Lindsten
Thomas B. Schon
C. Rasmussen
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Papers citing
"Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC"
50 / 56 papers shown
Title
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Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting
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Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference
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Yiyong Sun
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A projected nonlinear state-space model for forecasting time series signals
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Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
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Elias Morley
Claire Tomlin
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Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models
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Sergios Theodoridis
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03 Sep 2023
The Bayesian Context Trees State Space Model for time series modelling and forecasting
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Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
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Supitsana Srithasan
Armin Lederer
Sandra Hirche
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31 Mar 2023
Free-Form Variational Inference for Gaussian Process State-Space Models
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Edwin V. Bonilla
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Scott A. Sisson
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20 Feb 2023
Deep networks for system identification: a Survey
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Thomas B. Schon
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Towards Flexibility and Interpretability of Gaussian Process State-Space Model
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Output-Dependent Gaussian Process State-Space Model
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Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction
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51
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08 May 2022
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes
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Lars Imsland
M. Reble
Ehecatl Antonio del Rio Chanona
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Active Learning in Gaussian Process State Space Model
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Dingling Yao
Christoph Zimmer
Marc Toussaint
D. Nguyen-Tuong
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49
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30 Jul 2021
State-space aerodynamic model reveals high force control authority and predictability in flapping flight
Y. Bayiz
Bo Cheng
38
13
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14 Mar 2021
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective
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Stochastic embeddings of dynamical phenomena through variational autoencoders
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An Intuitive Tutorial to Gaussian Process Regression
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Prediction with Approximated Gaussian Process Dynamical Models
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Sandra Hirche
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Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data
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42
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Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
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76
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23 Apr 2020
Considering discrepancy when calibrating a mechanistic electrophysiology model
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Dominic G. Whittaker
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J. Walmsley
Keith Worden
Gary R. Mirams
Richard D. Wilkinson
72
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The Renyi Gaussian Process: Towards Improved Generalization
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140
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Structured Variational Inference in Unstable Gaussian Process State Space Models
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Felix Berkenkamp
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The Use of Gaussian Processes in System Identification
Simo Särkkä
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50
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13 Jul 2019
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
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Mark van der Wilk
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79
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Recursive Estimation for Sparse Gaussian Process Regression
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Dario Azzimonti
A. Benavoli
Marco Zaffalon
65
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28 May 2019
A novel Multiplicative Polynomial Kernel for Volterra series identification
Alberto Dalla Libera
R. Carli
G. Pillonetto
33
18
0
20 May 2019
Moment-Based Variational Inference for Markov Jump Processes
C. Wildner
Heinz Koeppl
65
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14 May 2019
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler
Duo Xu
64
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
71
9
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18 Dec 2018
Non-Factorised Variational Inference in Dynamical Systems
Alessandro Davide Ialongo
Mark van der Wilk
J. Hensman
C. Rasmussen
79
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
40
7
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24 Aug 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs
Haitao Liu
Yew-Soon Ong
Xiaobo Shen
Jianfei Cai
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144
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Learning dynamical systems with particle stochastic approximation EM
Andreas Svensson
Fredrik Lindsten
105
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A Local Information Criterion for Dynamical Systems
Arash Mehrjou
Friedrich Solowjow
Sebastian Trimpe
Bernhard Schölkopf
50
3
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27 May 2018
Specialized Interior Point Algorithm for Stable Nonlinear System Identification
Jack Umenberger
I. Manchester
54
33
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02 Mar 2018
Probabilistic Recurrent State-Space Models
Andreas Doerr
Christian Daniel
Martin Schiegg
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S. Schaal
Marc Toussaint
Sebastian Trimpe
106
123
0
31 Jan 2018
The Generalized Cross Validation Filter
Giulio Bottegal
G. Pillonetto
40
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Identification of Gaussian Process State Space Models
Stefanos Eleftheriadis
Tom Nicholson
M. Deisenroth
J. Hensman
94
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30 May 2017
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Guokun Lai
Wei-Cheng Chang
Yiming Yang
Hanxiao Liu
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119
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Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
Thomas B. Schon
Andreas Svensson
Lawrence M. Murray
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68
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An Efficient, Expressive and Local Minima-free Method for Learning Controlled Dynamical Systems
Ahmed S. Hefny
Carlton Downey
Geoffrey J. Gordon
31
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The interplay between system identification and machine learning
G. Pillonetto
63
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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
113
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A flexible state space model for learning nonlinear dynamical systems
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Thomas B. Schon
85
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System Identification through Online Sparse Gaussian Process Regression with Input Noise
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Thomas B. Schon
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129
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Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
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Arno Solin
Simo Särkkä
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88
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07 Jun 2015
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