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An Algorithm for Computing the Shannon-Capacity ofArbitrary Channels

  • Questions arising from Statistical Decision Theory, Bayes Methods and other probability theoretic fields lead to concepts of orthogonality of a family of probability measures. In this paper we therefore give a sketch of a generalized information theory which is very helpful in considering and answering those questions. In this adapted information theory Shannon's classical transition channels modelled by finite stochastic matrices are replaced by compact families of probability measures that are uniformly integrable. These channels are characterized by concepts such as information rate and capacity and by optimal priors and the optimal mixture distribution. For practical studies we introduce an algorithm to calculate the capacity of the whole probability family which is appli cable even for general output space. We then explain how the algorithm works and compare its numerical costs with those of the classical Arimoto-Blahut-algorithm.

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Metadaten
Author:Jürgen Krob, Holger Scholl
URN (permanent link):urn:nbn:de:hbz:386-kluedo-7309
Document Type:Preprint
Language of publication:English
Year of Completion:1993
Year of Publication:1993
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2000/04/03
Tag:Families of Probability Measures; Information Theory; Optimal Prior Distribution; Shannon-Capacity; Statistical Experiments; Structure Theory
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:5 Naturwissenschaften und Mathematik / 510 Mathematik
Licence (German):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011