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|a 10.1080/02664763.2020.1858273
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|a eng
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1 |
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|a Koloydenko, Alexey
|e verfasserin
|4 aut
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| 245 |
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|a MAP segmentation in Bayesian hidden Markov models
|b a case study
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|c 2022
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 16.07.2022
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|a published: Electronic-eCollection
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Informa UK Limited, trading as Taylor & Francis Group.
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|a We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data
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|a Journal Article
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|a Bayesian inference
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|a EM algorithm
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|a Hidden Markov model
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| 650 |
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|a MAP sequence
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| 650 |
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|a viterbi algorithm
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| 700 |
1 |
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|a Kuljus, Kristi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lember, Jüri
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Journal of applied statistics
|d 1991
|g 49(2022), 5 vom: 09., Seite 1203-1234
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|x 0266-4763
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|g year:2022
|g number:5
|g day:09
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|u http://dx.doi.org/10.1080/02664763.2020.1858273
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