Multiple hidden Markov models for categorical time series
| Autor: | Roberto Colombi, Sabrina Giordano |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Theoretical computer science Markov kernel Markov process Markov model Methodology (stat.ME) symbols.namesake Markov renewal process Conditional independence Econometrics Granger noncausality Markov properties Statistics - Methodology Mathematics Numerical Analysis Markov chain Variable-order Markov model Marginal models Multivariate Markov chains symbols Graphical models Markov property Hidden semi-Markov model Statistics Probability and Uncertainty Settore SECS-S/01 - Statistica |
| Zdroj: | Journal of Multivariate Analysis. 140:19-30 |
| ISSN: | 0047-259X |
| DOI: | 10.1016/j.jmva.2015.04.002 |
| Popis: | We introduce multiple hidden Markov models (MHMMs) where an observed multivariate categorical time series depends on an unobservable multivariate Mar- kov 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 to the latent and observable components of the MHMM, respectively. These Markov properties are also translated into zero restrictions on the parameters of marginal models for the transition probabilities and the distributions of the observable variables given the latent states. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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