We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a theoretically-justified variation of the EM algorithm developed within the framework of inference functions for margins. We illustrate the method using numerical experiments and an analysis of house occupancy.

翻译：我们建议对隐蔽的Markov模型(HMM)进行基于阴极的扩展,这种扩展适用于每次样本中记录的观测是多变的,由阴极扩展产生的联合模型允许根据从多重观测中获得的信息对隐蔽状态进行解码,然而,与独立的边缘情况不同,隐含于这种可能性的阴极依赖性结构带来了额外的计算挑战。我们利用在边际推理功能框架内开发的EM算法的理论上合理的变异来应对后者。我们用数字实验和对住房占用的分析来说明这种方法。