Infinite Structured Hidden Semi-Markov Models
Methodology
2014-07-02 v1 Applications
Machine Learning
Abstract
This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models. We focus on variants of Bayesian nonparametric hidden Markov models that enhance a posteriori state-persistence in particular. This paper also introduces a new Bayesian nonparametric framework for generating left-to-right and other structured, explicit-duration infinite hidden Markov models that we call the infinite structured hidden semi-Markov model.
Cite
@article{arxiv.1407.0044,
title = {Infinite Structured Hidden Semi-Markov Models},
author = {Jonathan H. Huggins and Frank Wood},
journal= {arXiv preprint arXiv:1407.0044},
year = {2014}
}
Comments
23 pages, 10 figures