Premium
Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method
Author(s) -
Robert C. P.,
Rydén T.,
Titterington D. M.
Publication year - 2000
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00219
Subject(s) - markov chain monte carlo , reversible jump markov chain monte carlo , markov chain , variable order bayesian network , hidden markov model , inference , markov model , variable order markov model , bayesian inference , statistical physics , computer science , bayesian probability , hidden semi markov model , monte carlo method , jump , mathematics , algorithm , artificial intelligence , statistics , machine learning , physics , quantum mechanics
Hidden Markov models form an extension of mixture models which provides a flexible class of models exhibiting dependence and a possibly large degree of variability. 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.