Identification of Gaussian process state-space models with particle stochastic approximation EM
| Autor: | Roger Frigola, Thomas B. Schön, Carl Edward Rasmussen, Fredrik Lindsten |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
FOS: Computer and information sciences
Computer science Maximum likelihood Bayesian probability Nonparametric statistics Machine Learning (stat.ML) Systems and Control (eess.SY) System identificationBayesianNon-parametric identificationGaussian processes Control Engineering Stochastic approximation Statistics::Computation symbols.namesake Statistics - Machine Learning Reglerteknik Expectation–maximization algorithm FOS: Electrical engineering electronic engineering information engineering symbols Computer Science - Systems and Control State space Statistical physics Gaussian process |
| Popis: | Gaussian process state-space models (GP-SSMs) are a very exible 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. CADICS |
| Databáze: | OpenAIRE |
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