Recursive parameter estimation algorithm of the Dirichlet hidden Markov model

The recursive (online, incremental) estimation of the hidden Markov model (HMM) parameters has become a more popular research subject. The complexity of the recursive methods is linear, and this complexity allows the estimation of parameters in real time. Most of the recursive parameter estimation m...

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Vydané v:Journal of statistical computation and simulation Ročník 90; číslo 2; s. 306 - 323
Hlavní autori: Vaičiulytė, Jūratė, Sakalauskas, Leonidas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 22.01.2020
Taylor & Francis Ltd
Predmet:
Algorithms
Clustering
Complexity
Computer simulation
Datasets
Dirichlet distribution
Dirichlet problem
hidden Markov models
likelihood method
Markov analysis
Markov chains
Mathematical models
Maximum likelihood method
Parameter estimation
recursive EM algorithm
Recursive methods
ISSN:0094-9655, 1563-5163
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Popis
Shrnutí:The recursive (online, incremental) estimation of the hidden Markov model (HMM) parameters has become a more popular research subject. The complexity of the recursive methods is linear, and this complexity allows the estimation of parameters in real time. Most of the recursive parameter estimation methods use Gaussian mixtures and do not explore other distributions. However, the underlying structure of the data might be non-Gaussian. Thus, we propose a novel recursive method for estimating the parameters of the Dirichlet HMM. The Dirichlet distribution is popular because of its flexibility in modelling data. The proposed estimation is based on the maximum likelihood method, which is known to give close to optimal results. The performance of our algorithm is tested using a computer simulation and the clustering of several data-sets. Several experiments were conducted in order to compare the performance of the Gaussian HMM and Dirichlet HMM in the classification of several data-sets.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2019.1679144