MAP segmentation in Bayesian hidden Markov models: a case study

Alexey Koloydenko, Kristi Kuljus, Jüri Lember

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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.
Original languageEnglish
Pages (from-to)1-32
Number of pages32
JournalJournal of Applied Statistics
Early online date10 Dec 2020
Publication statusE-pub ahead of print - 10 Dec 2020

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