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Activity Number: 254 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #305165
Title: Theoretical Guarantees of Convergence of EM Updates in Tangent Transformation Approach
Author(s): Indrajit Ghosh* and Anirban Bhattacharya and Prasenjit Ghosh and Debdeep Pati
Companies: Texas A&M University and TAMU and Texas A & M University and Texas A&M University
Keywords: Variational Approximations; Tangent Transformation

Variational approximation to posterior distributions has gained an enormous popularity in recent years as a computationally convenient alternative for Bayesian Inference. Beyond conjugate models, Variational approximations are challenging to implement and accordingly numerous majorization / minorization approaches are developed. In this poster, we focus on the tangent transform approach based on convex duality in generalized linear models. While such approaches and their variants are extremely popular, there are no theoretical guarantees on whether the Variational algorithm converges to the true parameter. In light of a recent developments of convergence of EM algorithm, we provide theoretical guarantees of the convergence of the Variational updates based on tangent transform approximations. In particular, we show that for random design matrix the updates converge to the truth with high probability.

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

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