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Optimal Information Processing and Bayes's Theorem
The American Statistician
Vol. 42, No. 4 (Nov., 1988), pp. 278-280
Stable URL: http://www.jstor.org/stable/2685143
Page Count: 3
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In this article statistical inference is viewed as information processing involving input information and output information. After introducing information measures for the input and output information, an information criterion functional is formulated and optimized to obtain an optimal information processing rule (IPR). For the particular information measures and criterion functional adopted, it is shown that Bayes's theorem is the optimal IPR. This optimal IPR is shown to be 100% efficient in the sense that its use leads to the output information being exactly equal to the given input information. Also, the analysis links Bayes's theorem to maximum-entropy considerations.
The American Statistician © 1988 American Statistical Association