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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Asymptotics of Approximate Bayesian Computation

## Asymptotics of Approximate Bayesian ComputationAdd to your list(s) Download to your calendar using vCal - Paul Fearnhead (Lancaster University)
- Wednesday 05 July 2017, 09:00-09:45
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. SINW01 - Scalable statistical inference Many statistical applications involve models for which are easy to sample from, but for which it is difficult to evaluate the likelihood. Approximate Bayesian computation is a likelihood-free method for implementing Bayesian inference in such cases. This talk will overview some recent results on the theoretical properties of approximate Bayesian computation which consider the performance of ABC as we get more data. It will cover questions such as: when does the ABC posterior concentrate on the true parameter value? What distribution does the ABC posterior converge to? And what is the frequentist distribution of point-estimates derived using ABC . It will also cover the impact of Monte Carlo error on estimates obtained using ABC , and consider whether, asympotically, it is possible to efficiently estimate parameters using ABC if we have a fixed Monte Carlo sample size. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
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