Bornn, LukePillai, NateshSmith, AaronWoodard, Dawn2018-01-292016Bornn, Luke, Natesh S. Pillai, Aaron Smith, and Dawn Woodard. 2016. “The Use of a Single Pseudo-Sample in Approximate Bayesian Computation.” Statistics and Computing 27 (3) (March 14): 583–590. doi:10.1007/s11222-016-9640-7.0960-3174http://nrs.harvard.edu/urn-3:HUL.InstRepos:34744107We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For the rejection sampling version of ABC, it is known that multiple pseudo-samples cannot substantially increase (and can substantially decrease) the efficiency of the algorithm as compared to employing a high-variance estimate based on a single pseudo-sample. We show that this conclusion also holds for a Markov chain Monte Carlo version of ABC, implying that it is unnecessary to tune the number of pseudo-samples used in ABC-MCMC. This conclusion is in contrast to particle MCMC methods, for which increasing the number of particles can provide large gains in computational efficiency.en-USJournal Article10.1007/s11222-016-9640-7