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This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) doi:10.2307/2291223 we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
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