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SUMMARY:Lazy ABC - Prangle\, D (University of Reading)
DTSTART:20140424T132000Z
DTEND:20140424T135500Z
UID:TALK52168@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:In approximate Bayesian computation (ABC) algorithms\, paramet
 er proposals are accepted if corresponding simulated datasets are sufficie
 ntly close to the observations. Producing the large quantity of model simu
 lations needed requires considerable computer time. However\, it is often 
 clear early on in a simulation that it is unlikely to produce a close matc
 h. This talk is on an ABC algorithm which saves time by abandoning such si
 mulations early. A probabilistic stopping rule is used which leaves the ta
 rget distribution unchanged from that of standard ABC. Applications of thi
 s idea beyond ABC are also discussed.\n
LOCATION:Seminar Room 1\, Newton Institute
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