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Principle of indifference - Wikipedia

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The principle of indifference (also called principle of insufficient reason) is a rule for assigning epistemic probabilities. The principle of indifference states that in the absence of any relevant evidence, agents should distribute their credence (or "degrees of belief") equally among all the possible outcomes under consideration.[1] In Bayesian probability, this is the simplest non-informative prior. The principle of indifference is meaningless under the frequency interpretation of probability,[citation needed] in which probabilities are relative frequencies rather than degrees of belief in uncertain propositions, conditional upon state information. The textbook examples for the application of the principle of indifference are coins, dice, and cards. In a macroscopic system, at least, it must be assumed that the physical laws that govern the system are not known well enough to predict the outcome. As observed some centuries ago by John Arbuthnot (in the preface of Of the Laws of Cha

Principle of indifference - Wikipedia Jump to content From Wikipedia, the free encyclopedia In probability theory, a rule for assigning epistemic probabilities This article includes a list of general references but lacks corresponding inline citations . Please help improve this article by introducing more precise citations. ( April 2010 ) ( Learn how and when to remove this message ) Part of a series on Bayesian statistics Posterior = Likelihood × Prior ÷ Evidence Background Bayesian inference Bayesian probability Bayes' theorem Bernstein–von Mises theorem Coherence Cox's theorem Cromwell's ru

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