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Consistency of discrete Bayesian learning
2008
Theoretical Computer Science
Bayes' rule specifies how to obtain a posterior from a class of hypotheses endowed with a prior and the observed data. There are three fundamental ways to use this posterior for predicting the future: marginalization (integration over the hypotheses w.r.t. the posterior), MAP (taking the a posteriori most probable hypothesis), and stochastic model selection (selecting a hypothesis at random according to the posterior distribution). If the hypothesis class is countable, and contains the data
doi:10.1016/j.tcs.2008.06.038
fatcat:fesrb5p7wbhojndnbvippza7yq