Abstract
The hidden Markov model (HMM) provides an attractive framework for modeling long-term persistence in a variety of applications including pattern recognition. Unlike typical mixture models, hidden Markov states can represent the heterogeneity in data and it can be extended to a multivariate case using a hierarchical Bayesian approach. This article provides a nonparametric Bayesian modeling approach to the multi-site HMM by considering stick-breaking priors for each row of an infinite state transition matrix. This extension has many advantages over a parametric HMM. For example, it can provide more flexible information for identifying the structure of the HMM than parametric HMM analysis, such as the number of states in HMM. We exploit a simulation example and a real dataset to evaluate the proposed approach.
Original language | English |
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Pages (from-to) | 4896-4907 |
Number of pages | 12 |
Journal | Communications in Statistics Part B: Simulation and Computation |
Volume | 46 |
Issue number | 6 |
DOIs | |
State | Published - 3 Jul 2017 |
Keywords
- Bayesian analysis
- Hierarchical modeling
- Multi-site hidden Markov model
- Stick-breaking prior