HMM熵计算的熵半环前后向算法
Entropy Semiring Forward-backward Algorithm for HMM Entropy Computation
摘要：
提出了一种熵半环前向后向算法（ESRFB），并将其应用于在给定观测序列的情况下，计算隐马尔可夫模型（HMM）的子序列约束熵和状态序列熵。ESRFB基于熵半环上的前向-后向递归，在相同的时间复杂度下，比Mann和MacCallum提出的算法具有更低的内存需求.此外，当它仅与前向传递一起使用时，它适用于给定观察序列的HMM熵的计算，具有与Hernando等人先前开发的算法相同的时间和存储复杂度
英文摘要：
The paper presents Entropy Semiring Forwardbackward algorithm (ESRFB) and its application for memory efficient computation of the subsequence constrained entropy and state sequence entropy of a Hidden Markov Model (HMM) when an observation sequence is given. ESRFB is based on forward-backward recursion over the entropy semiring, having the lower memory requirement than the algorithm developed by Mann and MacCallum, with the same time complexity. Furthermore, when it is used with forward pass only, it is applicable for the computation of HMM entropy for a given observation sequence, with the same time and memory complexity as the previously developed algorithm by Hernando et al
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