ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2020
Scalable Bayesian Inference for Coupled Hidden Markov and Semi-Markov Models.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Modelling methicillin-resistantJournal of the Royal Society, Interface · 2022Trial
- Dynamics of trachoma infection in West Africa revealed by a hidden state model.PLoS computational biology · 2026Article
- Non-centering for discrete-valued state transition models: an application to ESBL-producingStatistics and computing · 2026Article
- Simulation based composite likelihood.Statistics and computing · 2025Article
- Efficient modelling of infectious diseases in wildlife: A case study of bovine tuberculosis in wild badgers.PLoS computational biology · 2024Article
- Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.Epidemics · 2022Article
- Uncovering ecological state dynamics with hidden Markov models.Ecology letters · 2020Review
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Authors and funding
3 authors.
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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 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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.