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Touloupou, Panayiota, Finkenstädt, Bärbel and Spencer, Simon E. F. (2019) Scalable Bayesian inference for coupled hidden Markov and semi-Markov models. Journal of Computational and Graphical Statistics . doi:10.1080/10618600.2019.1654880 (In Press)
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WRAP-scalable-Bayesian-inference-coupled-hidden-Markov-models-Spencer-2019.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 20 August 2020. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (701Kb) |
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 | ||||||
|---|---|---|---|---|---|---|---|
| Divisions: | Faculty of Science > Statistics | ||||||
| Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||||
| Publisher: | American Statistical Association | ||||||
| ISSN: | 1537-2715 | ||||||
| Official Date: | 20 August 2019 | ||||||
| Dates: |
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| Date of first compliant deposit: | 2 August 2019 | ||||||
| DOI: | 10.1080/10618600.2019.1654880 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| 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 | ||||||
| Funder: | PhD scholarship from the Department of Statistics at the University of Warwick. PT and SEFS gratefully acknowledge funding of the NTD Modelling Consortium by the Bill and Melinda Gates Foundation [OPP1152057, OPP1053230, OPP1156227]. | ||||||
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