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Activity Number: 665
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #320285
Title: Scalable Bayesian Variable Selection and Model Averaging Under Block Orthogonal Design
Author(s): Omiros Papaspiliopoulos* and David Rossell
Companies: and University of Warwick
Keywords: forward search ; normalising constants ; tree-based search ; numerical integration ; multi-modality

We devise a new framework for fully Bayesian variable selection and model averaging that computationally scales linearly with the number of predictors and involves no MCMC. Our approach exploits an asummed block-diagonal structure of the Gramm matrix, which might there be by design (as in wavelets, PCA regression, or certain experimental designs) or it might be the result of a pre-processing of the predictors. At the stated cost our approach returns posterior model inclusion probabilities, the highest posterior probability model, and Bayesian model averaged estimates of parameters

This is joint work with David Rossell (Warwick)

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

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