Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models

Bayesian inference for coupled hidden Markov models frequently relies on data augmentation techniques for imputation of the hidden state processes. Considerable progress has been made on developing such techniques, mainly using Markov chain Monte Carlo (MCMC) methods. However, as the dimensionality...

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Veröffentlicht in:	Journal of computational and graphical statistics Jg. 29; H. 2; S. 238 - 249
Hauptverfasser:	Touloupou, Panayiota, Finkenstädt, Bärbel, Spencer, Simon E. F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Taylor & Francis 02.04.2020 Taylor & Francis Ltd
Schlagworte:	Algorithms Bayesian analysis Computer simulation Coupled hidden Markov model Data augmentation E coli Epidemics Forward-backward algorithm Markov analysis Markov chain Monte Carlo methods Markov chains Monte Carlo simulation Scalable and Efficient Computation Statistical inference Epidemics Data augmentation Coupled hidden Markov model Markov chain Monte Carlo methods Forward–backward algorithm
ISSN:	1061-8600, 1537-2715
Online-Zugang:	Volltext
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