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Activity Number: 155
Type: Invited
Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #307268
Title: Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Author(s): Anoop Korattikara Balan*+ and Yutian Chen and Max Welling
Companies: University of California and University of California, Irvine and University of Amsterdam
Keywords: MCMC ; Metropolis-Hastings ; Big Data ; Sequential testing
Abstract:

Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints twice in order to reach a single binary decision is computationally inefficient. We introduce an approximate Metropolis-Hastings rule based on a sequential hypothesis test which allows us to accept or reject samples with high confidence using only a fraction of the data required for the exact MH rule. While this introduces an asymptotic bias, we show that this bias can be controlled and is more than offset by a decrease in variance due to our ability to draw more samples per unit of time.


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