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Activity Number: 211
Type: Roundtables
Date/Time: Monday, August 10, 2015 : 12:30 PM to 1:50 PM
Sponsor: Section on Statistical Computing
Abstract #315329
Title: Bayesian Computation for High-Dimensional Data Sets
Author(s): Naveen Narisetty*
Companies: University of Michigan
Keywords: High Dimensional Data ; Bayesian Computation ; Variable Selection ; MCMC ; Gibbs Sampling
Abstract:

Bayesian methods for high-dimensional data are being studied increasingly, providing new insights and advantages associated with these methods. Outreach of Bayesian methods to large data sets such as gene expression would heavily depend on availability of efficient computational algorithms. In this roundtable, we will discuss some of the existing Bayesian methods and computational algorithms for high-dimensional data sets. More specifically, we will focus on the linear regression set-up and consider the problems of estimation and variable selection. Although the theoretical properties of Bayesian methods for high-dimensional data are of independent interest, the emphasis of this discussion will be on computational issues such as the speed and scalability of the algorithms.


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

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