Bayesian inference in hidden Markov models through reversible jump Markov chain Monte Carlo (0)
| Venue: | Journal of the Royal Statistical Society, Series B |
| Citations: | 36 - 2 self |
BibTeX
@ARTICLE{Robert_bayesianinference,
author = {Christian P. Robert and Tobias Rydén and D.M. Titterington},
title = {Bayesian inference in hidden Markov models through reversible jump Markov chain Monte Carlo},
journal = {Journal of the Royal Statistical Society, Series B},
year = {},
volume = {62},
pages = {57--75}
}
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Abstract
Hidden Markov models form an extension of mixture models providing a flexible class of models exhibiting dependence and a possibly large degree of variability. In this paper we show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number of components of a hidden Markov model in a Bayesian framework. We employ a mixture of zero mean normal distributions as our main example and apply this model to three sets of data from finance, meteorology and geomagnetism, respectively. AMS 1991 classification. Primary 62M09. Secondary 62F15, 62P05. Key words. Hidden Markov model, Bayesian inference, model selection, Markov chain Monte Carlo. 1 Introduction Hidden Markov models (HMMs) have been used in many areas as convenient representations of weakly dependent heterogeneous phenomena; a few examples are econometrics (Hamilton, 1989; Chib, 1996; Krolzig, 1997; Billio et al., 1998), finance (Ryd'en et al., 1998), biology (Leroux and Puter...
