Authors
Jos Uffink
University of Minnesota
Abstract
The principle of maximum entropy is a general method to assign values to probability distributions on the basis of partial information. This principle, introduced by Jaynes in 1957, forms an extension of the classical principle of insufficient reason. It has been further generalized, both in mathematical formulation and in intended scope, into the principle of maximum relative entropy or of minimum information. It has been claimed that these principles are singled out as unique methods of statistical inference that agree with certain compelling consistency requirements. This paper reviews these consistency arguments and the surrounding controversy. It is shown that the uniqueness proofs are flawed, or rest on unreasonably strong assumptions. A more general class of inference rules, maximizing the so-called Re[acute ]nyi entropies, is exhibited which also fulfill the reasonable part of the consistency assumptions.
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DOI 10.1016/1355-2198(95)00015-1
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References found in this work BETA

The Well-Posed Problem.Edwin T. Jaynes - 1973 - Foundations of Physics 3 (4):477-493.
A Problem for Relative Information Minimizers in Probability Kinematics.Bas C. van Fraassen - 1981 - British Journal for the Philosophy of Science 32 (4):375-379.
Entropy and Uncertainty.Teddy Seidenfeld - 1986 - Philosophy of Science 53 (4):467-491.

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Citations of this work BETA

Generalizing the Lottery Paradox.Igor Douven & Timothy Williamson - 2006 - British Journal for the Philosophy of Science 57 (4):755-779.
Entropy - A Guide for the Perplexed.Roman Frigg & Charlotte Werndl - 2011 - In Claus Beisbart & Stephan Hartmann (eds.), Probabilities in Physics. Oxford University Press. pp. 115-142.

View all 19 citations / Add more citations

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