Computer Science – Information Theory
Scientific paper
May 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001aipc..568..340k&link_type=abstract
BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 20th International Workshop. AIP Conference Proceedi
Computer Science
Information Theory
1
Data Analysis: Algorithms And Implementation, Data Management, Information Theory And Communication Theory
Scientific paper
We examine the relationship between the Bayesian and information-theoretic formulations of source separation algorithms. This work makes use of the relationship between the work of Claude E. Shannon and the ``Recent Contributions'' by Warren Weaver (Shannon & Weaver 1949) as clarified by Richard T. Cox (1979) and expounded upon by Robert L. Fry (1996) as a duality between a logic of assertions and a logic of questions. Working with the logic of assertions requires the use of probability as a measure of degree of implication. This leads to a Bayesian formulation of the problem. Whereas, working with the logic of questions requires the use of entropy as a measure of the bearing of a question on an issue leading to an information-theoretic formulation of the problem. .
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