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Lazy ABC

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If you have a question about this talk, please contact Mustapha Amrani .

Advanced Monte Carlo Methods for Complex Inference Problems

In approximate Bayesian computation (ABC) algorithms, parameter proposals are accepted if corresponding simulated datasets are sufficiently close to the observations. Producing the large quantity of model simulations needed requires considerable computer time. However, it is often clear early on in a simulation that it is unlikely to produce a close match. This talk is on an ABC algorithm which saves time by abandoning such simulations early. A probabilistic stopping rule is used which leaves the target distribution unchanged from that of standard ABC . Applications of this idea beyond ABC are also discussed.

This talk is part of the Isaac Newton Institute Seminar Series series.

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