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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Popescu, C., Wong, Y.S. (2004). Monte Carlo Approach for Switching State-Space Models. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_97
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DOI: https://doi.org/10.1007/978-3-540-24677-0_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
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