Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
This work addresses the challenge of parameter estimation in flexible Bayesian nonparametric models for dynamical systems, which is an incremental improvement in computational methods for system identification.
The paper tackles the problem of maximum likelihood identification of parameters in Gaussian process state-space models (GP-SSMs) for nonlinear dynamical systems, and presents a method based on stochastic approximation EM with particle Markov chain Monte Carlo, achieving efficient identification while retaining the full nonparametric description of the dynamics.
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.