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，2014，8（3）：359-367.
ISSN 1673-9418 CODEN JKYTA8
Journal of Frontiers of Computer Science and Technology
1673-9418/2014/08(03)-0359-09
doi: 10.3778/j.issn.1673-9418.1309004
E-mail: fcst@vip.163.com
http://www.ceaj.org
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非齐次隐马尔可夫因子模型期望最大化算法
*
程玉胜
1+
，丁美文
1
，夏叶茂
2
1. 安庆师范学院 数学与计算科学学院，安徽 安庆 246011
2. 南京林业大学 应用数学系，南京 210037
Expectation-Maximization Algorithm about Non-Homogenous Hidden Markov
Factor Model
CHENG Yusheng
1+
, DING Meiwen
1
, XIA Yemao
2
1. School of Mathematics & Computation Sciences, Anqing Normal University, Anqing, Anhui 246011, China
2. Department of Applied Mathematics, Nanjing Forestry University, Nanjing 210037, China
+ Corresponding author: E-mail: chengyshaq@163.com
CHENG Yusheng, DING Meiwen, XIA Yemao. Expectation-maximization algorithm about non-homogenous
hidden Markov factor model. Journal of Frontiers of Computer Science and Technology, 2014, 8(3)：359-367.
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.
Key words: hidden Markov model; latent variable model; expectation-maximization (EM); forward-backward recursion
* The National Natural Science Foundation of China under Grant No. 61306046 (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).
Received 2013-09, Accepted 2013-12.
CNKI 网络优先出版：2013-12-05, http://www.cnki.net/kcms/detail/11.5602.TP.20131205.1429.002.html
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