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Activity Number: 512 - Bayesian Model Selection
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #324495 View Presentation
Title: Bayesian Regression with Proximal Algorithm
Author(s): Lei Sun* and Nicholas Polson
Companies: University of Chicago and University of Chicago
Keywords: structured sparsity ; proximal algorithm ; nonconvex ; Bayes MAP ; trend filter ; double Pareto
Abstract:

We develop proximal algorithms for Bayesian regularized regression. Proximal algorithms are useful for solving difficult optimization problems with composite objective functions, especially when they involve nonsmooth functions. We develop a fast implementation with our proximal framework and can induce structured sparsity in the coefficients. We apply our methodology to a variety of problems, including signal recovery, nonlinear quantile regression, and variable selection. We compare our procedure with standard computational methods, such as $L_1$ trend filter, spike-and-slab, and the Bayesian bridge.


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

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