Bayesian clustering for continuous-time hidden Markov models |
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Authors: | Yu Luo David A. Stephens David L. Buckeridge |
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Affiliation: | 1. Department of Mathematics, King's College London, London, U.K;2. Department of Mathematics and Statistics, McGill University, Montreal, Canada;3. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada |
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Abstract: | 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. |
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Keywords: | Continuous-time hidden Markov models mixture models model-based clustering nonparametric Bayesian inference reversible-jump MCMC split–merge proposal. MSC 2020: Primary 60J22 62F15 secondary 62H30 |
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