贝叶斯隐马尔可夫模型中的地图分割：一个案例研究
MAP segmentation in Bayesian hidden Markov models: a case study.
作者信息
Koloydenko Alexey, Kuljus Kristi, Lember Jüri
机构信息
Royal Holloway, University of London, London, UK.
Institute of Mathematics and Statistics, University of Tartu, Estonia.
出版信息
J Appl Stat. 2020 Dec 10;49(5):1203-1234. doi: 10.1080/02664763.2020.1858273. eCollection 2022.
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.
我们考虑在贝叶斯框架下，针对具有有限状态和有限发射字母表的隐马尔可夫模型（HMM）估计最大后验概率（MAP）状态序列的问题，其中发射矩阵和转移矩阵都具有狄利克雷先验。我们研究了一个由数千个蛋白质比对序列对组成的训练集。该训练数据用于设置贝叶斯MAP分割的先验超参数。由于维特比算法不再适用，因此没有简单的方法来找到MAP路径，我们考虑并比较了几种迭代算法。本文的主要目标是将贝叶斯框架与频率主义框架进行对比测试，在频率主义框架中，HMM的参数是通过训练数据进行估计的。
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