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SUMMARY:Asymptotics for ABC algorithms - Judith  Rousseau (University of O
 xford\; Université Paris-Dauphine)
DTSTART:20180118T140000Z
DTEND:20180118T144500Z
UID:TALK97789@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Approximate Bayesoan Computation  algorithms (ABC) are used in
  cases where the likelihood is intractable. To simulate  from the (approxi
 mate) posterior distribution a possiblity is to sample new data from the m
 odel and check is these new data are close in some sense to the true data.
  The output of this algorithms thus depends on how we define the notion of
  closeness\, which is based on a choice of summary statistics and on a thr
 eshold. Inthis work we study the behaviour of the algorithm under the assu
 mption that the summary statistics are concentrating on some deterministic
  quantity and characterize the asymptotic behaviour of the resulting appro
 ximate posterior distribution in terms of the threshold and the rate of co
 ncentration of the summary statistics. The case of misspecified models is 
 also treated where we show that surprising asymptotic behaviour appears.
LOCATION:Seminar Room 1\, Newton Institute
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