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• W308423894 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." @default.
• W308423894 created "2016-06-24" @default.
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• W308423894 date "2014-04-25" @default.
• W308423894 modified "2023-09-26" @default.
• W308423894 title "A Pseudo-Marginal Perspective on the ABC Algorithm" @default.
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• W308423894 hasPublicationYear "2014" @default.
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