The use of a single pseudo-sample in approximate Bayesian computation

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...

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Bibliographic Details
Published in:Statistics and computing 2017-05, Vol.27 (3), p.583-590
Main Authors: Bornn, Luke, Pillai, Natesh S., Smith, Aaron, Woodard, Dawn
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bayesian analysis
Mathematics and Statistics
Probability and Statistics in Computer Science
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
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Summary: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.
ISSN:0960-3174
1573-1375
DOI:10.1007/s11222-016-9640-7