计算机科学与探索Issue(3)：359-367,9.DOI:10.3778/j.issn.1673-9418.1309004
非齐次隐马尔可夫因子模型期望最大化算法
Expectation-Maximization Algorithm about Non-Homogenous Hidden Markov Factor Model
摘要
Abstract
Latent variable model plays an important role in characterizing interrelationship among factor variables and constructing relationships between factor and observed variable. However, in real applications, data set often takes on the temporal variability, multimode, skewness, and so on. This paper extends the classic latent variable model to the latent variable model mixed with non-homogenous hidden Markov model. In order to avoid integral about complete data, this paper introduces the expectation-maximization (EM) algorithm to calculate the likelihood function. At the same time, this paper presents the corresponding statistics using the Akaike information criterion and the Bayes information criterion to select appropriate model, which effectively solves the estimation problem in the hidden Markov model. Finally, the experiments are carried out in the mental-health data and the results show that the method is effective.关键词
隐马尔可夫模型/潜变量模型/期望最大化(EM)/向前向后递推Key words
hidden Markov model/latent variable model/expectation-maximization (EM)/forward-backward recursion分类
信息技术与安全科学引用本文复制引用
程玉胜,丁美文,夏叶茂..非齐次隐马尔可夫因子模型期望最大化算法[J].计算机科学与探索,2014,(3):359-367,9.基金项目
The National Natural Science Foundation of China under Grant No.61306046(国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Anhui Province of China under Grant Nos.070412061,10040606Q42(安徽省自然科学基金) （安徽省自然科学基金）
