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Activity Number: 506
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #319286
Title: Bayesian Computation in Dirichlet Process Mixture Models
Author(s): Erina Paul* and Sanjib Basu
Companies: and Northern Illinois University
Keywords: Approximate Bayesian Computation ; Dirichlet process ; non-conjugate prior

Dirichlet process mixture (DPM) provides a flexible and popular model for nonparametric Bayesian analysis with a substantial literature on Markov chain sampling and other computation methods for conjugate and non-conjugate DPM models. In recent years, approximate Bayesian computation methods have been successfully applied in Bayesian analysis of complex models where the likelihood function is analytically unavailable or computationally intractable. These methods provide samples from a distribution which is close to the target posterior distribution of interest. We propose an efficient computation method for DPM using approximate Bayesian computation and illustrate the performance of the proposed method in real dataset.

Authors who are presenting talks have a * after their name.

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