(21th-June-2020)
• Rather than choosing the most likely model or delineating the set of all models that are consistent with the training data, another approach is to compute the posterior probability of each model given the training examples.
• The idea of Bayesian learning is to compute the posterior probability distribution of the target features of a new example conditioned on its input features and all of the training examples.
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