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高校应用数学学报  2015, Vol. 30 Issue (1): 17-30    
    
隐马尔可夫因子分析模型的半参数贝叶斯分析
夏业茂, 勾建伟, 刘应安
南京林业大学 理学院, 江苏南京 210037
Semi-parametric Bayesian analysis for factor analysis model mixed with hidden Markov model
XIA Ye-mao, GOU Jian-wei, LIU Ying-an
School of Sci., Nanjing Forestry Univ., Nanjing 210037, China
 全文: PDF 
摘要: 因子模型在刻画潜在因素(因子)与观测变量间的影响关系并进而解释多元观测指标( 变量)间的相关性方面具有重要作用. 在实际应用中, 观测数据往往呈现出时序变异多峰, 偏态等特性. 将经典的因子分析延伸到带有时齐隐马尔可夫模型的动力因子模型, 并建立了半参数贝叶斯分析程序. 分块GIBBS抽样器用以后验抽样. 经验结果展示所建立的统计程序是有效的.
关键词: 隐马尔可夫模型因子分析模型半参数贝叶斯分块GIBBS抽样器    
Abstract: Factor analysis model plays an important role in characterizing dependence of latent factors on the observed variables and interpreting the correlation among the observed indexes (variables). However, in real applications, data set often takes on the temporal variability, multimode, skewness, and so on. In this paper, we extended the classic factor analysis model to the dynamic factor model mixed with homogenous hidden Markov model, and developed a Bayesian semiparametric analysis procedure. Blocked Gibbs sampler is used to implement posterior sampling. The empirical results show that our method is effective.
Key words: hidden Markov model    factor analysis model    Bayesian semiparametric analysis    blocked Gibbs sampler
收稿日期: 2014-07-26 出版日期: 2018-06-06
CLC:  O212.8  
基金资助: 国家自然科学基金(11471161); 南京林业大学高学历人才项目(163101004); 南京市科技创新择优资助项目(013101001)
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夏业茂
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引用本文:

夏业茂, 勾建伟, 刘应安. 隐马尔可夫因子分析模型的半参数贝叶斯分析[J]. 高校应用数学学报, 2015, 30(1): 17-30.

XIA Ye-mao, GOU Jian-wei, LIU Ying-an. Semi-parametric Bayesian analysis for factor analysis model mixed with hidden Markov model. Applied Mathematics A Journal of Chinese Universities, 2015, 30(1): 17-30.

链接本文:

http://www.zjujournals.com/amjcua/CN/        http://www.zjujournals.com/amjcua/CN/Y2015/V30/I1/17

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