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Abstract

An information-processing representation of statistical inference is formulated and utilized to derive an optimal information-processing rule. When particular input and output information measures and an information criterion functional are employed, the derived optimal information-processing rule is Bayes' Theorem. It is also shown that Bayes' Theorem is a 100% efficient information-processing rule.

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