The posterior probability of a model given data, P(H|D), is given by P(H|D) = P(D|H)P(H)/P(D)

The key data-dependent term, P(D|H), is sometimes called the evidence for model H, and evaluating it correctly is the key to Bayesian model comparison. The evidence is usually the normalizing constant or partition function of another inference, namely the inference of the parameters of model H given the data D.

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