High-order extensions of the double chain Markov model

High-order extensions of the double chain Markov model

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Article ID: iaor20041150
Country: United States
Volume: 18
Issue: 2
Start Page Number: 1933
End Page Number: 227
Publication Date: Apr 2002
Journal: Stochastic Models
Authors:
Abstract:

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of a young monkeys, show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.

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