Bayesian Inference in Hidden Markov Models Through the Reversible Jump Markov Chain Monte Carlo Method
- 1 January 2000
- journal article
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 62 (1) , 57-75
- https://doi.org/10.1111/1467-9868.00219
Abstract
Summary: 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.This publication has 0 references indexed in Scilit: