Infinite Structured Hidden Semi-Markov Models
This work addresses the problem of modeling complex temporal dependencies in sequential data for researchers in machine learning and statistics, but it appears incremental as it builds on existing Bayesian nonparametric methods.
The paper reviews Bayesian nonparametric techniques for infinite hidden Markov models and introduces a new framework for generating structured, explicit-duration infinite hidden Markov models called the infinite structured hidden semi-Markov model, focusing on enhancing state-persistence.
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.