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54 – Privacy Preserving Record Linkage
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
Anoop Korattikara Balan
University of California
Yutian Chen
University of California, Irvine
Max Welling
University of Amsterdam
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.