Jos Uffink
University of Minnesota
The principle of maximum entropy is a method for assigning values to probability distributions on the basis of partial information. In usual formulations of this and related methods of inference one assumes that this partial information takes the form of a constraint on allowed probability distributions. In practical applications, however, the information consists of empirical data. A constraint rule is then employed to construct constraints on probability distributions out of these data. Usually one adopts the rule that equates the expectation values of certain functions with their empirical averages. There are, however, various other ways in which one can construct constraints from empirical data, which makes the maximum entropy principle lead to very different probability assignments. This paper shows that an argument by Jaynes to justify the usual constraint rule is unsatisfactory and investigates several alternative choices. The choice of a constraint rule is also shown to be of crucial importance to the debate on the question whether there is a conflict between the methods of inference based on maximum entropy and Bayesian conditionalization.
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DOI 10.1016/1355-2198(95)00022-4
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References found in this work BETA

The Logic of Chance.John Venn - 1876 - Dover Publications.
Bayesian Conditionalisation and the Principle of Minimum Information.P. M. Williams - 1980 - British Journal for the Philosophy of Science 31 (2):131-144.
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.

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Citations of this work BETA

Compendium of the Foundations of Classical Statistical Physics.Jos Uffink - 2005 - In Jeremy Butterfield & John Earman (eds.), Handbook of the Philosophy of Physics. Elsevier.
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.
An Empirical Approach to Symmetry and Probability.Jill North - 2010 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 41 (1):27-40.

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