Bayesian Inference in Hidden Markov Models through Jump Markov Chain Monte Carlo.

Hidden Markov models is an extension of mixture models providing a flexible class of models exhibiting dependence and a possibly large degree of variability. In this paper the authors show how 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.

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Year of publication:	1999
Authors:	Robert, C.P. ; Ryden, T. ; Titterington, D.M.
Subject:	ECONOMETRICS \| STATISTICAL ANALYSIS \| ECONOMIC MODELS

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Type of publication:	Book / Working Paper
Notes:	25 pages
Classification:	C50 - Econometric Modeling. General ; C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005780743