论文标题
马尔可夫观察模型
Markov Observation Models
论文作者
论文摘要
在此，隐藏的马尔可夫模型将扩展，以允许马尔可夫链观测。特别是，假定观察值是马尔可夫链，其一个步骤过渡概率取决于隐藏的马尔可夫链。为这种更一般的模型开发了对Baum-Welch算法的预期最大化类似物，以估计隐藏状态和观测值的过渡概率，并估算初始关节隐藏状态分布的概率。信仰状态或过滤器递归跟踪隐藏状态，然后是由于该期望最大化算法的计算而产生的。还开发了一个与Viterbi算法的动态编程类似物，以估计鉴于观测值序列，最可能的隐藏状态序列。
Herein, the Hidden Markov Model is expanded to allow for Markov chain observations. In particular, the observations are assumed to be a Markov chain whose one step transition probabilities depend upon the hidden Markov chain. An Expectation-Maximization analog to the Baum-Welch algorithm is developed for this more general model to estimate the transition probabilities for both the hidden state and for the observations as well as to estimate the probabilities for the initial joint hidden-state-observation distribution. A believe state or filter recursion to track the hidden state then arises from the calculations of this Expectation-Maximization algorithm. A dynamic programming analog to the Viterbi algorithm is also developed to estimate the most likely sequence of hidden states given the sequence of observations.
