Generalized Baum-Welch and Viterbi Algorithms Based on the Direct Dependency among Observations

Authors
1 Department of Statistics, Faculty of mathematics and Computer Sciences, Allameh Tabataba'i University, Tehran, Iran.
2 Centre of New Technologies, University of Warsaw, Banacha 2c Street, 02-097 Warsaw, Poland
3 Financial Mathematics Group, Faculty of Financial Sciences, University of Kharazmi, Tehran, Iran
Abstract
The parameters of a Hidden Markov Model (HMM) are transition and emission probabilities‎. ‎Both can be estimated using the Baum-Welch algorithm‎. ‎The process of discovering the sequence of hidden states‎, ‎given the sequence of observations‎, ‎is performed by the Viterbi algorithm‎. ‎In both Baum-Welch and Viterbi algorithms‎, ‎it is assumed that‎, ‎given the states‎, ‎the observations are independent from each other‎. ‎In this paper‎, ‎we first consider the direct dependency between consecutive observations in the HMM‎, ‎and then use conditional independence relations in the context of a Bayesian network which is a probabilistic graphical model for generalizing the Baum-Welch and Viterbi algorithms‎. ‎We compare the performance of the generalized algorithms with the commonly used ones in simulation studies for synthetic data‎. ‎We finally apply these algorithms on real data sets which are related to biological and inflation data‎. ‎We show that the generalized Baum-Welch and Viterbi algorithms significantly outperform the conventional ones when sample sizes become larger‎.
Keywords

Volume 17, Issue 2
December 2018
Pages 205-225

  • Receive Date 23 July 2022