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: | Berchtold Andr |
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.