Journal of the Royal Statistical Society. Series B, Statistical methodology, 2000, Vol.62 (1), p.57-75
2000
Details
- Autor(en) / Beteiligte
- Titel
- Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method
- Ist Teil von
- Journal of the Royal Statistical Society. Series B, Statistical methodology, 2000, Vol.62 (1), p.57-75
- Ort / Verlag
- Oxford, UK and Boston, USA: Blackwell Publishers Ltd
- Erscheinungsjahr
- 2000
- Link zum Volltext
- Quelle
- EBSCOhost Business Source Ultimate
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1369-7412eISSN: 1467-9868DOI: 10.1111/1467-9868.00219Titel-ID: cdi_proquest_miscellaneous_38837288
- Format
- –
- Schlagworte
- Bayesian inference, Bayesian method, Covariance, Density estimation, Distribution, Distribution theory, Economic models, Exact sciences and technology, Hidden Markov model, Inference, Jacobians, Markov chain Monte Carlo methods, Markov chains, Markov models, Markov processes, Markovian processes, Mathematics, Maximum likelihood estimation, Model selection, Modelling, Monte Carlo simulation, Parametric inference, Probability and statistics, Probability theory and stochastic processes, Sciences and techniques of general use, Standard and Poors 500 Index, Standard deviation, Statistics