CUED Publications database

Identification of Gaussian process state-space models with particle stochastic approximation EM

Frigola, R and Lindsten, F and Schön, TB and Rasmussen, CE (2014) Identification of Gaussian process state-space models with particle stochastic approximation EM. In: UNSPECIFIED pp. 4097-4102..

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Abstract

© IFAC. Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:45
Last Modified: 03 Aug 2017 03:15
DOI: