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Activity Number: 28 - Advances in Bayesian Theory and Methods on Network Data Modeling
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #313208
Title: Optimization for Bayesian Inference
Author(s): Leo Duan*
Companies: University of Florida

In Bayesian inference, optimization is mostly used as a tool for point estimation or variational inference. In this talk, I will first introduce a new computational method named "Transport Monte Carlo" that targets the exact posterior distribution. This method uses fast optimization to learn a non-parametric mixture of one-to-one transformations, between the sample from the posterior and the one from an iid uniform distribution. I will demonstrate much-improved performances in often-encountered sampling problems, such as high-dimensional regression and combinatorial estimation in graph modeling. In the second part of the talk, I will introduce a new class of prior/likelihood that lacks closed-form but are defined through optimization. This expands Bayesian inference for uncertainty quantification in a much broader class of models such as linear trend filtering of multivariate time series.

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

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