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Activity Number: 131 - Simulation and MCMC
Type: Contributed
Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #329120 Presentation
Title: Variational Approximation for Importance Sampling
Author(s): Xiao Su* and Yuguo Chen
Companies: University of Illinois at Urbana Champaign and University of Illinois at Urbana-Champaign
Keywords: Importance sampling; Variational approximation; f-divergence

In this paper, we propose an importance sampling algorithm with proposal distribution obtained from variational approximation. This method combines the strength of both importance sampling and variational Bayesian method. On one hand, this method avoids the bias from variational method. On the other hand, variational approximation provides a way to design the proposal distribution for the importance sampling algorithm. Theoretical justification of the proposed method is provided. Numerical results show that using variational approximation as the proposal can improve the performance of importance sampling and sequential importance sampling.

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

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