Bayesian Methods for Hidden Markov Models Recursive Computing in the 21st Century

Markov chain Monte Carlo (MCMC) sampling strategies can be used to simulate hidden Markov model (HMM) parameters from their posterior distribution given observed data. Some MCMC methods used in practice (for computing likelihood, conditional probabilities of hidden states, and the most likely sequen...

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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 97; no. 457; pp. 337 - 351
Main Author:	Scott, Steven L
Format:	Journal Article
Language:	English
Published:	Alexandria, VA Taylor & Francis 01.03.2002 American Statistical Association Taylor & Francis Ltd
Subjects:	21st century Algorithms Bayes estimators Bayesian analysis Bayesian method Computers Data sampling Distribution theory Exact sciences and technology Forward-backward recursion Fraud Gibbs sampler Gross national product Inference from stochastic processes; time series analysis Kalman filter Local computation Markov analysis Markov chain Monte Carlo Markov chains Markov models Markov processes Markovian processes Mathematics Methods Metropolitan areas Modeling Monte Carlo simulation Parametric models Polytopes Probability Probability and statistics Probability theory and stochastic processes Recursion Review Paper Sciences and techniques of general use Statistical analysis Statistical methods Statistics Mixed distribution Conditional distribution Statistical analysis Kalman filter Mixture model Statistical estimation Gibbs sampling Poisson distribution Asymptotic convergence MCMC algorithm Marginal distribution Log likelihood Hidden Markov model Recursive method Predictive value Forward backward recursion Bayes methods Computing method Posterior distribution
ISSN:	0162-1459, 1537-274X
Online Access:	Get full text
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