- Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
- 2013 Joint Statistical Meetings (JSM)
- Book/source title
- 2013 JSM proceedings: papers presented at the Joint Statistical Meetings, Montréal, Québec, Canada, August 3-8, 2013, and other ASA-sponsored conferences [cd-rom]
- Pages (from-to)
- Alexandria, Virginia: American Statistical Association
- Document type
- Conference contribution
- Faculty of Science (FNWI)
- Informatics Institute (IVI)
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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