Introduction
Abstract
A hidden semi-Markov model (HSMM) can be considered as an extension of a hidden Markov model (HMM) by allowing the underlying process to be a semi-Markov process, or an extension of a semi-Markov process by allowing the states to be hidden and their emissions to be observable. The conditional dependencies among the random variables of an HSMM can be described by a directed acyclic graph (dynamic Bayesian network: DBN) or an undirected probabilistic graphical model (conditional random field: CRF). This chapter reviews all these models that are closely related to HSMMs, including Markov renewal process, semi-Markov process, HMMs, DBNs, and CRFs. Then the concepts and terms of HSMMs are illustrated, and the history ...
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