Bayesian inference in hidden markov models through reversible jump Markov
chain Monte Carlo
C.P. Robert, T.Rydén and D.M. Titterington
Department of Mathematical Statistics,
Lund Institute of Technology,
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.
Hidden Markov model, Bayesian inference, model selection, Markov chain Monte