DPP: Inference of Parameters of Normal Distributions from a Mixture of Normals

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.

Package details

AuthorLuis M. Avila [aut, cre], Michael R. May [aut], Jeff Ross-Ibarra [aut]
MaintainerLuis M. Avila <lmavila@gmail.com>
LicenseMIT + file LICENSE
Package repositoryView on CRAN
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DPP documentation built on May 6, 2019, 1:10 a.m.