论文标题
具有隐藏状态和无法观察到过渡的概率系统
Probabilistic Systems with Hidden State and Unobservable Transitions
论文作者
论文摘要
我们考虑具有隐藏状态和无法观察到的过渡的概率系统，这是隐藏的Markov模型（HMMS）的扩展，该模型尤其允许无法观察到的ε-过渡（也称为无效的过渡），从而允许观察者不知道的状态变化。由于存在ε环，此附加特征使理论复杂化，并需要仔细设置相应的概率空间和随机变量。特别是，我们提出了一种用于确定观察结果的最可能解释的算法（对HMM的Viterbi算法的概括）和一种用于适应基于观察的给定模型概率的参数学习方法（Baum-Welch algorithm的概括）。后一种算法确保给定的观察值在调整参数后具有较高（或相等）的概率，其正确性可以直接从所谓的EM算法得出。
We consider probabilistic systems with hidden state and unobservable transitions, an extension of Hidden Markov Models (HMMs) that in particular admits unobservable ε-transitions (also called null transitions), allowing state changes of which the observer is unaware. Due to the presence of ε-loops this additional feature complicates the theory and requires to carefully set up the corresponding probability space and random variables. In particular we present an algorithm for determining the most probable explanation given an observation (a generalization of the Viterbi algorithm for HMMs) and a method for parameter learning that adapts the probabilities of a given model based on an observation (a generalization of the Baum-Welch algorithm). The latter algorithm guarantees that the given observation has a higher (or equal) probability after adjustment of the parameters and its correctness can be derived directly from the so-called EM algorithm.
