维特比对齐的精度
On the accuracy of the Viterbi alignment
摘要：
在隐马尔可夫模型中，底层的马尔可夫链通常是隐藏的。通常，使用极大似然比对（Viterbi比对）作为其估计。虽然具有最大的可能性，但维特比对齐可以通过传递最意想不到的状态来表现得非常不典型。为了避免这种情况，可以通过强制维特比对齐不通过这些状态来修改维特比对齐。在这篇文章中，一个迭代的过程，提高维特比对齐的建议和研究。迭代的方法相比，一个简单的束的方法，其中一些状态与低概率都在同一时间被替换。可以看出，调整维特比对准的迭代方式更有效，并且其具有优于聚束方法的若干优点。用于改进维特比对齐的相同迭代算法可以用于窥视的情况，即当可以揭示隐藏状态时。此外，在不同的模型参数的条件下的维特比对齐的分类概率的下界进行了研究。
英文摘要：
In a hidden Markov model, the underlying Markov chain is usually hidden. Often, the maximum likelihood alignment (Viterbi alignment) is used as its estimate. Although having the biggest likelihood, the Viterbi alignment can behave very untypically by passing states that are at most unexpected. To avoid such situations, the Viterbi alignment can be modified by forcing it not to pass these states. In this article, an iterative procedure for improving the Viterbi alignment is proposed and studied. The iterative approach is compared with a simple bunch approach where a number of states with low probability are all replaced at the same time. It can be seen that the iterative way of adjusting the Viterbi alignment is more efficient and it has several advantages over the bunch approach. The same iterative algorithm for improving the Viterbi alignment can be used in the case of peeping, that is when it is possible to reveal hidden states. In addition, lower bounds for classification probabilities of the Viterbi alignment under different conditions on the model parameters are studied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
