Monte Carlo approach for switching state-space models

  • Authors:

  • Cristina Popescu;Yau Shu Wong

  • Affiliations:

  • Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada;Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada

  • Venue:
  • IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
  • Year:
  • 2004

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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.