Estimation of Viterbi path in Bayesian hidden Markov models. / Lember, Jüri; Gasbarra, Dario; Koloydenko, Alexey; Kuljus, Kristi.

In: Metron, Vol. 77, No. 2 , 08.2019, p. 137-169.

Research output: Contribution to journalArticle

Published

Documents

  • Accepted Manuscript

    Accepted author manuscript, 237 KB, PDF document

    Embargo ends: 28/05/20

Abstract

The article studies different methods for estimating the Viterbi path in the Bayesian framework. The Viterbi path is an estimate of the underlying state path in hidden Markov models (HMMs), which has a maximum posterior probability (MAP). For an HMM with given parameters, the Viterbi path can be easily found with the Viterbi algorithm. In the Bayesian framework the Viterbi algorithm is not applicable and several iterative methods can be used instead. We introduce a new EM-type algorithm for finding the MAP path and compare it with various other methods for finding the MAP path, including the variational Bayes approach and MCMC methods. Examples with simulated data are used to compare the performance of the methods. The main focus is on non-stochastic iterative methods and our results show that the best of those methods work as well or better than the best MCMC methods. Our results demonstrate that when the primary goal is segmentation, then it is more reasonable to perform segmentation directly by considering the transition and emission parameters as nuisance parameters.
Original languageEnglish
Pages (from-to)137-169
Number of pages33
JournalMetron
Volume77
Issue number2
Early online date28 May 2019
DOIs
Publication statusPublished - Aug 2019
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 28900561