Recursive estimation of multivariate hidden Markov model parameters

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 techni...

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Bibliographic Details
Published in:	Computational statistics Vol. 34; no. 3; pp. 1337 - 1353
Main Authors:	Vaičiulytė, Jūratė, Sakalauskas, Leonidas
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2019 Springer Nature B.V
Subjects:	Algorithms Complexity Computer simulation Economic Theory/Quantitative Economics/Mathematical Methods Machine learning Markov analysis Markov chains Mathematics and Statistics Normal distribution Original Paper Parameter estimation Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Statistical analysis Statistics Transition probabilities Likelihood method Clustering Hidden Markov models Recursive EM algorithm
ISSN:	0943-4062, 1613-9658
Online Access:	Get full text
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