Normalizing constant: Difference between revisions

Revision as of 18:43, 1 May 2009

Revision as of 01:35, 31 August 2009

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==Bayes' theorem==

[[Bayes' theorem]] says that the posterior probability measure is proportional to the product of the prior probability measure and the [[likelihood function]] . ''Proportional to'' implies that one must multiply or divide by a normalizing constant to assign measure 1 to the whole space, i.e., to get a probability measure. In a simple discrete case we have

:<math>P(H_0|D) = \frac{P(D|H_0)P(H_0)}{P(D)}</math>