Hierarchical DP Mixture
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چکیده
The model is a DP mixture of normals for related random probability measures Hj . Each random measure is assumed to arise as a mixture Hj = F0 + (1 − )Fj of one common distribution F0 and a distribution Fj that is specific to the j-th submodel. See Mueller, Quintana and Rosner (2004) for details of the model. In summary, the implemented model is as follows. Without loss of generality we assume that each submodel corresponds to a different study in a set of related studies. Let θij denote the i-th observation in the j-th study (we use θ, assuming that the model would typically be used for a random effects distribution). We assume that θji, i = 1, . . . , nj are samples from a random probability measure for the j-th study, which in turn is a mixture of a measure F0 that is common to all studies, and an idiosyncratic measure Fj that is specific to the j-th study. θji ∼ F0 + (1− )Fj
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