Lecture 20 | Bayesian analysis
de nes a parametric model with parameter , as in our previous lectures.1 The joint distri-bution of and Xis then the product f X; (x; ) = f Xj (xj )f ( ); 1For notational simplicity, we are considering here a single data value X, but this extends naturally to the case where X = (X 1;:::;X n) is a data vector and f Xj (xj ) is the joint ...
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