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Bayesian clustering for continuous‐time hidden Markov models.
- Source :
-
Canadian Journal of Statistics . Mar2023, Vol. 51 Issue 1, p134-156. 23p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *HIDDEN Markov models
*MARKOV processes
*MARKOV chain Monte Carlo
*GIBBS sampling
Subjects
Details
- Language :
- English
- ISSN :
- 03195724
- Volume :
- 51
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Canadian Journal of Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 161968399
- Full Text :
- https://doi.org/10.1002/cjs.11671