Cp. Robert et al., Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method, J ROY STA B, 62, 2000, pp. 57-75
Citations number
25
Categorie Soggetti
Mathematics
Journal title
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
Hidden Markov models form an extension of mixture models which provides a f
lexible class of models exhibiting dependence and a possibly large degree o
f variability. We show how reversible jump Markov chain Monte Carte techniq
ues can be used to estimate the parameters as well as the number of compone
nts of a hidden Markov model in a Bayesian framework. We employ a mixture o
f zero-mean normal distributions as our main example and apply this model t
o three sets of data from finance, meteorology and geomagnetism.