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Bayesian inference for Hidden Markov Model

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  • Rosella Castellano

    (University of Macerata)

  • Luisa Scaccia

    (University of Macerata)

Abstract

Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of a model, as well as the number of regimes. We consider a mixture of normal distributions characterized by different means and variances under each regime, extending the model proposed by Robert et al. (2000), based on a mixture of zero mean normal distributions.

Suggested Citation

  • Rosella Castellano & Luisa Scaccia, 2007. "Bayesian inference for Hidden Markov Model," Working Papers 43-2007, Macerata University, Department of Finance and Economic Sciences, revised Oct 2008.
    • Handle: RePEc:mcr:wpdief:wpaper00043
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    References listed on IDEAS

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