Monte Carlo approach for switching state-space models

Abstract

In this paper we present a Monte Carlo EM algorithm for learning the parameters of a state-space model with a Markov switching. Since the expectations in the E step are intractable, we consider an implementation based on the Gibbs sample. The rate of convergence is improved using a nesting algorithm and Rao-Blackwellised forms. We illustrate the performance of the proposed method for simulated and experimental physiological data.