The Use of a Single Pseudo-Sample in Approximate Bayesian Computation

Luke Bornn; Natesh Pillai; Aaron Smith; Dawn Woodard

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arXiv · 1404.6298

The Use of a Single Pseudo-Sample in Approximate Bayesian Computation

Luke Bornn·Natesh Pillai·Aaron Smith·Dawn Woodard

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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.

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BibTeX RIS

Luke Bornn, Natesh Pillai, Aaron Smith, Dawn Woodard. 2016-02-16. The Use of a Single Pseudo-Sample in Approximate Bayesian Computation. https://arxiv.org/abs/1404.6298

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