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Activity Number: 441
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319451
Title: Computationally Tractable Approximate and Smoothed Polya Trees
Author(s): William Cipolli* and Timothy Hanson
Companies: Colgate University and University of South Carolina
Keywords: Bayesian Nonparametric ; Density Estimation ; GLMM ; Meta-analysis

A discrete approximation to the Polya tree prior suitable for latent data is proposed that enjoys surprisingly simple and effcient conjugate updating. This approximation is illustrated in two applied contexts: the implementation of a nonparametric meta-analysis involving studies on the relationship between alcohol consumption and breast cancer, and random intercept Poisson regression for Ache armadillo hunting treks. The discrete approximation is then smoothed with Gaussian kernels to provide a smooth density for use with continuous data; the smoothed approximation is illustrated on a classic dataset on galaxy velocities and on recent data involving breast cancer survival in Louisiana.

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

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