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Activity Number: 455 - Recent Advances in Bayesian Computation: Theory and Methods
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #304397 Presentation
Title: Weighted Batch Means Estimators in Markov Chain Monte Carlo
Author(s): James Flegal*
Companies: University of California, Riverside
Keywords: MCMC; Bayesian Computation; Variance Estimation; Central Limit Theorem

We propose a family of weighted batch means variance estimators, which are computationally efficient and can be conveniently applied in practice. The focus is on Markov chain Monte Carlo simulations and estimation of the asymptotic covariance matrix in the Markov chain central limit theorem, where conditions ensuring strong consistency are provided. Finite sample performance is evaluated through auto-regressive, Bayesian spatial-temporal, and Bayesian logistic regression examples, where the new estimators show significant computational gains with a minor sacrifice in variance compared with existing methods.

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

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