MAP segmentation in Bayesian hidden Markov models: a case study

作     者：Koloydenko, Alexey Kuljus, Kristi Lember, Juri 

作者机构：Royal Holloway Univ London London England Univ Tartu Inst Math & Stat Tartu Estonia 

出 版 物：《JOURNAL OF APPLIED STATISTICS》 (应用统计学杂志)

年 卷 期：2022年第49卷第5期

页      面：1203-1234页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Estonian Research [IUT34-5] CouncilPRG865 

主　　题：Hidden Markov model Bayesian inference MAP sequence viterbi algorithm EM algorithm 

摘      要：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.