Multiple hidden Markov models for categorical time series

JOURNAL OF MULTIVARIATE ANALYSIS

卷:140

Article

Colombi, R.¹  Giordano, S.² 

[1] Univ Bergamo, Dept Engn, Bergamo, Italy

[2] Univ Calabria, Dept Econ Stat & Finance, I-87030 Commenda Di Rende, Italy

关键词: Conditional independence;    Granger noncausality;    Graphical models;    Marginal models;    Markov properties;    Multivariate Markov chains;   

DOI  :  10.1016/j.jmva.2015.04.002

来源: Elsevier

【 摘 要 】

We introduce multiple hidden Markov models (MHMMs) where a multivariate categorical time series depends on a latent multivariate Markov chain. MHMMs provide an elegant framework for specifying various independence relationships between multiple discrete time processes. These independencies are interpreted as Markov properties of a mixed graph and a chain graph associated respectively to the latent and observation components of the MHMM. These Markov properties are also translated into zero restrictions on the parameters of marginal models for the transition probabilities and the distributions of observable variables given the latent states. (C) 2015 Elsevier Inc. All rights reserved.

【 授权许可】

Free