Triplet Markov Chains

  • Pieczynski W
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Abstract

The Hidden Markov Chains (HMC) are widely applied in various problems. This succes is mainly due to the fact that the hidden process can be recovered even in the case of very large set of data. These models have been recetly generalized to 'Pairwise Markov Chains' (PMC) model, which admit the same processing power and a better modeling one. The aim of this note is to propose further generalization called Triplet Markov Chains (TMC), in which the distribution of the couple (hidden process, observed process) is the marginal distribution of a Markov chain. Similarly to HMC, we show that posterior marginals are still calculable in Triplets Markov Chains. We provide a necessary and sufficient condition that a TMC is a PMC, which shows that the new model is strictly more general. Furthermore, a link with the Dempster-Shafer fusion is specified. © 2002 Académie des sciences/Éditions scientifiques et médicales Elsevier SAS.

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APA

Pieczynski, W. (2002). Triplet Markov Chains. Comptes Rendus Mathematique, 335(3), 275–278. https://doi.org/10.1016/S1631-073X(02)02462-7

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