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Activity Number: 102
Type: Invited
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #318372
Title: Bayesian Additive Regression Trees: Extensions and Embedding in Complex Models
Author(s): Jared S. Murray*
Companies: Carnegie Mellon University
Keywords: BART ; Bayesian Nonparametrics ; Regression Trees

Bayesian additive regression trees (BART) are a popular method for inducing a nonparametric prior over regression functions, and their existing incarnations have seen application in a wide array of applied settings. BART models are appealing due to their flexibility, modularity and computational tractability. In this talk I will describe a number of extensions to existing ``vanilla'' BART models. These include loglinear models for counts, classification and heteroscedastic regression; models where the regression function parameterizes the distribution of a latent variable; and models where latent variables appear as inputs to the regression function itself.

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

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