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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Vydané v:Journal of computational and graphical statistics Ročník 29; číslo 2; s. 238 - 249
Hlavní autori: Touloupou, Panayiota, Finkenstädt, Bärbel, Spencer, Simon E. F.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Taylor & Francis 02.04.2020
Taylor & Francis Ltd
Predmet:
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
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Shrnutí: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 and complexity of the hidden processes increase some of these methods become inefficient, either because they produce MCMC chains with high autocorrelation or because they become computationally intractable. Motivated by this fact we developed a novel MCMC algorithm, which is a modification of the forward filtering backward sampling algorithm, that achieves a good balance between computation and mixing properties, and thus can be used to analyze models with large numbers of hidden chains. Even though our approach is developed under the assumption of a Markovian model, we show how this assumption can be relaxed leading to minor modifications in the algorithm. Our approach is particularly well suited to epidemic models, where the hidden Markov chains represent the infection status of an individual through time. The performance of our method is assessed on simulated data on epidemic models for the spread of Escherichia coli O157:H7 in cattle. Supplementary materials for this article are available online.
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Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JCGS.
Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JCGS.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2019.1654880