Entropy computation in partially observed Markov chains

François Desbouvries

doi:10.1063/1.2423294

Entropy computation in partially observed Markov chains

François Desbouvries

CNRS SAMOVAR UMR 5157

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

Résumé

Let X = X_nn∈N be a hidden process and Y = Y _nn∈N be an observed process. We assume that (X,Y) is a (pairwise) Markov Chain (PMC). PMC are more general than Hidden Markov Chains (HMC) and yet enable the development of efficient parameter estimation and Bayesian restoration algorithms. In this paper we propose a fast (i.e., O(N)) algorithm for computing the entropy of X_nn=0^N given an observation sequence y_nn=0^N.

langue originale	Anglais
Pages (de - à)	355-357
Nombre de pages	3
journal	AIP Conference Proceedings
Volume	872
Les DOIs	https://doi.org/10.1063/1.2423294
état	Publié - 27 déc. 2006
Modification externe	Oui

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10.1063/1.2423294

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KW - Hidden Markov models

KW - Partially observed Markov chains

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Entropy computation in partially observed Markov chains

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