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