CUED Publications database

Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

Frigola, R and Lindsten, F and Schön, TB and Rasmussen, CE (2013) Bayesian inference and learning in Gaussian process state-space models with Particle MCMC. Advances in Neural Information Processing Systems. ISSN 1049-5258

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Abstract

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learning (i.e. state estimation and system identification) in nonlinear nonparametric state-space models. We place a Gaussian process prior over the state transition dynamics, resulting in a flexible model able to capture complex dynamical phenomena. To enable efficient inference, we marginalize over the transition dynamics function and, instead, infer directly the joint smoothing distribution using specially tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the smoothing distribution is computed, the state transition predictive distribution can be formulated analytically. Our approach preserves the full nonparametric expressivity of the model and can make use of sparse Gaussian processes to greatly reduce computational complexity.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:16
Last Modified: 10 Aug 2017 01:37
DOI: