The Use of a Single Pseudo-Sample in Approximate Bayesian Computation
Abstract
We 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.
Cite
@article{arxiv.1404.6298,
title = {The Use of a Single Pseudo-Sample in Approximate Bayesian Computation},
author = {Luke Bornn and Natesh Pillai and Aaron Smith and Dawn Woodard},
journal= {arXiv preprint arXiv:1404.6298},
year = {2016}
}