CAJ | 학술논문

潜变量模型在刻画因子之间的相互关系以及因子与观测变量之间的关联性时具有重要作用。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，因此将经典的潜变量模型延伸到非齐次隐马尔可夫潜变量模型，并且为避免对完全数据的积分计算，将期望最大化（expectation-maximization，EM）算法引入到似然函数的计算上；采用Akaike信息准则和Bayes信息准则选择合适的模型，提出了相应的统计计算和检验方法，有效解决了隐马尔可夫模型中的最大估算似然函数问题；最后选择心理-健康数据进行了实验，实验结果表明该方法是有效的。
잠변량모형재각화인자지간적상호관계이급인자여관측변량지간적관련성시구유중요작용。재실제응용중，관측수거왕왕정현출시서변이、다봉、편태등특성，인차장경전적잠변량모형연신도비제차은마이가부잠변량모형，병차위피면대완전수거적적분계산，장기망최대화（expectation-maximization，EM）산법인입도사연함수적계산상；채용Akaike신식준칙화Bayes신식준칙선택합괄적모형，제출료상응적통계계산화검험방법，유효해결료은마이가부모형중적최대고산사연함수문제；최후선택심리-건강수거진행료실험，실험결과표명해방법시유효적。
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.