Recursive estimation of multivariate hidden Markov model parameters

Jūratė Vaičiulytė () and Leonidas Sakalauskas ()
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Jūratė Vaičiulytė: Vilnius University
Leonidas Sakalauskas: Klaipėda University

Computational Statistics, 2019, vol. 34, issue 3, No 17, 1337-1353

Abstract: Abstract This article addresses a recursive parameter estimation algorithm for a hidden Markov model (HMM). The work focuses on an HMM with multiple states that are assumed to follow from a multivariate Gaussian distribution. The novelty of this study lies in a state transition probability calculation technique that simplifies the application of the backward stage of the forward–backward algorithm. For sequential observation analysis, the complexity of the created recursive algorithm for learning the HMM parameters is merely linear. Meanwhile, the classical Baum–Welch algorithm has second order complexity; therefore, it cannot be applied in online analysis situations. The properties of the proposed recursive expectation–maximization (EM) algorithm were explored by a computer simulation solving test examples and demonstrate that this algorithm can be efficiently applied to solve online tasks related to HMM parameter estimation.

Keywords: Hidden Markov models; Likelihood method; Recursive EM algorithm; Clustering (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00180-019-00877-z

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