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Activity Number: 157
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321094
Title: Fast Sampling with Gaussian Scale-Mixture Priors
Author(s): Anirban Bhattacharya*
Companies: Texas A&M University
Keywords: Bayesian ; shrinkage ; scalable ; sparsity

We propose an efficient way to sample from a class of structured multivariate Gaussian distributions which routinely arise as conditional posteriors of model parameters that are assigned a conditionally Gaussian prior. The proposed algorithm only requires matrix operations in the form of matrix multiplications and linear system solutions. We exhibit that the computational complexity of the proposed algorithm grows linearly with the dimension unlike existing algorithms relying on Cholesky factorizations with cubic orders of complexity. Various applications are illustrated.

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

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