Entropy Computation in Partially Observed Markov Chains - Archive ouverte HAL
Communication Dans Un Congrès Année : 2006

Entropy Computation in Partially Observed Markov Chains

Résumé

Let X = {X n } n∈IN be a hidden process and Y = {Y n } n∈IN 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 n } N n=0 given an observation sequence {y n } N n=0 .
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Dates et versions

hal-04415402 , version 1 (24-01-2024)

Identifiants

Citer

François Desbouvries. Entropy Computation in Partially Observed Markov Chains. Bayesian Inference and Maximum Entropy Methods In Science and Engineering, MaxEnt, Jul 2006, Paris, France. pp.355-357, ⟨10.1063/1.2423294⟩. ⟨hal-04415402⟩
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