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Scalable Bayesian inference for coupled hidden Markov and semi-Markov models
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Touloupou, Panayiota, Finkenstädt, Bärbel and Spencer, Simon E. F. (2020) Scalable Bayesian inference for coupled hidden Markov and semi-Markov models. Journal of Computational and Graphical Statistics, 29 (2). 238-249 . doi:10.1080/10618600.2019.1654880
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WRAP-scalable-Bayesian-inference-coupled-hidden-Markov-models-Spencer-2019.pdf - Accepted Version - Requires a PDF viewer. Download (701Kb) | Preview |
Official URL: https://doi.org/10.1080/10618600.2019.1654880
Abstract
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 analyse 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.
| Item Type: | Journal Article | |||||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics | |||||||||||||||||||||
| Divisions: | Faculty of Science > Statistics | |||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Hidden Markov models , Monte Carlo method, Markov processes, Bayesian statistical decision theory | |||||||||||||||||||||
| Journal or Publication Title: | Journal of Computational and Graphical Statistics | |||||||||||||||||||||
| Publisher: | American Statistical Association | |||||||||||||||||||||
| ISSN: | 1537-2715 | |||||||||||||||||||||
| Official Date: | 2020 | |||||||||||||||||||||
| Dates: |
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| Volume: | 29 | |||||||||||||||||||||
| Number: | 2 | |||||||||||||||||||||
| Page Range: | 238-249 | |||||||||||||||||||||
| DOI: | 10.1080/10618600.2019.1654880 | |||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||
| Publication Status: | Published | |||||||||||||||||||||
| Publisher Statement: | “This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Computational and Graphical Statistics on 20/08/2019, available online: http://www.tandfonline.com/10.1080/10618600.2019.1654880 | |||||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
| RIOXX Funder/Project Grant: |
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