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Luo, Yu; Stephens, David A.; Buckeridge, David L.

Bayesian clustering for continuous-time hidden Markov models. (English. French summary) Zbl 07759523

Can. J. Stat. 51, No. 1, 134-156 (2023).
Summary: We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized linear observation model. Specifically, in this article we carry out finite and infinite mixture model-based clustering for a CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). For a finite mixture model with a prior on the number of components, we implement reversible-jump MCMC to facilitate the trans-dimensional move between models with different numbers of clusters. For a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to improve the performance of the MCMC algorithm. We apply our proposed algorithms to simulated data as well as a real-data example, and the results demonstrate the desired performance of the new sampler.
{© 2021 Statistical Society of Canada}

MSC:

62-XX Statistics
60J22 Computational methods in Markov chains
62F15 Bayesian inference
62H30 Classification and discrimination; cluster analysis (statistical aspects)

Keywords:

continuous-time hidden Markov models; mixture models; model-based clustering; nonparametric Bayesian inference; reversible-jump MCMC; split-merge proposal

Software:

BayesianMixtures.jl
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Full Text: DOI arXiv

References:

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