Hidden semi-Markov Model

In this paper, we explore the class of the Hidden Semi-Markov Model (HSMM), a flexible extension of the popular Hidden Markov Model (HMM) that allows the underlying stochastic process to be a semi-Markov chain. A major motivation of this paper is to provide methods to estimate HSMMs (1) in a computationally feasible time, (2) in an exact manner, i.e. only subject to Monte Carlo error, and (3) in a sequential setting.

My doctoral research evolves around the topic of Sequential Bayesian Learning for State Space Models. More generally, I am working on estimating model parameters in a batch as well as in a times series setting, with a focus on latent variable models. This thesis includes projects with Hidden Semi-Markov Models, Copulae, and Epidemic Models that have latent factors attached.

Introduction to Hidden semi-Markov Models