The evaluation problem in discrete semi-hidden Markov models

This paper is devoted to discrete semi-hidden Markov models (SHMM), which are related to the well-known hidden Markov models (HMM). In particular, the HMM  associated to an SHMM  is defined, and the forward algorithm for solving the evaluation problem in SHMMs is introduced. Experiments show that in...

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Bibliographic Details
Published inMathematics and computers in simulation Vol. 137; pp. 350 - 365
Main Authors Gómez-Lopera, J.F., Martínez-Aroza, J., Román-Roldán, R., Román-Gálvez, R., Blanco-Navarro, D.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2017
Subjects
Evaluation problem
Forward algorithm
Hidden Markov models
Semi-hidden Markov models
Symbolic run sequences
Forward algorithm
Symbolic run sequences
Semi-hidden Markov models
Hidden Markov models
Evaluation problem
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Summary:This paper is devoted to discrete semi-hidden Markov models (SHMM), which are related to the well-known hidden Markov models (HMM). In particular, the HMM  associated to an SHMM  is defined, and the forward algorithm for solving the evaluation problem in SHMMs is introduced. Experiments show that in a set of randomly generated sequences with different SHMMs, the maximum value for the conditional probability of each sequence being generated by the model most frequently matches the model that generated the sequence. Something similar happens to associated HMMs, suggesting that the HMM  associated to a given SHMM  shows a certain affinity to this, which is higher than other HMMs.
ISSN:0378-4754
1872-7166
DOI:10.1016/j.matcom.2016.12.002