论文标题
贝叶斯隐藏马尔可夫模型中的地图分割：案例研究
MAP segmentation in Bayesian hidden Markov models: a case study
论文作者
论文摘要
我们考虑了估算有限状态和有限发射字母隐藏马尔可夫模型（HMM）的最大后验概率（MAP）状态序列的问题，在贝叶斯设置中，发射和过渡矩阵都具有差异的先验。我们研究了一个由数千种蛋白质对齐对组成的训练集。训练数据用于设置贝叶斯地图分割的先前的超参数。由于Viterbi算法不再适用，因此没有简单的程序来查找地图路径，并且考虑并比较了几种迭代算法。本文的主要目的是测试贝叶斯设置对频率主义者的设置，其中使用训练数据估算了HMM的参数。
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.
