Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7547687 | Statistical Methodology | 2016 | 44 Pages |
Abstract
In this paper, we present a non-parametric continuous density Hidden Markov mixture model (CDHMMix model) with unknown number of mixtures for blind segmentation or clustering of sequences. In our presented model, the emission distributions of HMMs are chosen to be Gaussian with full, diagonal, or tridiagonal covariance matrices. We apply a Bayesian approach to train our presented model and drive the inference of our model using the Monte Carlo Markov Chain (MCMC) method. For the multivariate Gaussian emission a method that maintains the tridiagonal structure of the covariance is introduced. Moreover, we present a new sampling method for hidden state sequences of HMMs based on the Viterbi algorithm that increases the mixing rate.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Najmeh Bathaee, Hamid Sheikhzadeh,