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Activity Number: 434
Type: Invited
Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #310516
Title: Particle Bayes
Author(s): Nicholas Polson*+
Companies: Booth School of Business
Keywords: Bayes ; Sparsity

Particle Bayesian methods are developed for high dimensional posterior inference. An efficient particle pruning algorithm is constructed for high dimensional model and parameter search. We illustrate the efficiency of our approach in hierarchical sparsity and mixture Kalman filtering with learning. (Joint work with Brandon Willard).

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

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