# Bayesian Clustering for Continuous-Time Hidden Markov Models

arXiv: https://arxiv.org/abs/1906.10252 · HTML: https://mlsanity.com/paper/1906.10252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 1 of 1)

- [Markovletics: Methods and A Novel Application for Learning Continuous-Time Markov Chain Mixtures](https://mlsanity.com/paper/2402.17730/) (2024)

## Abstract (arXiv)

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 paper, 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 prior on the number of components, we implement reversible-jump MCMC to facilitate the trans-dimensional move between different number of clusters. For a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ proposed algorithms to the simulated data as well as a real data example, and the results demonstrate the desired performance of the new sampler.