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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Inference with approximate likelihoods - Helen Ogd
en (University of Southampton)
DTSTART;TZID=Europe/London:20170703T153000
DTEND;TZID=Europe/London:20170703T161500
UID:TALK73131AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/73131
DESCRIPTION:In cases where it is infeasible to compute the lik
elihood exactly\, an alternative is to find some
numerical approximation to the likelihood\, then
to use this approximate likelihood in place of the
exact likelihood to do inference about the model
parameters. This is a fairly commonly used appro
ach\, and I will give several examples of approxim
ate likelihoods which have been used in this way.
But is this a valid approach to inference? I wil
l give conditions under which inference with an a
pproximate likelihood shares some of the same asym
ptotic properties as inference with the exact lik
elihood\, and describe the implications in some e
xamples. I will finish with some ideas about how t
o construct scalable likelihood approximations wh
ich give statistically valid inference.

LOCATION:Seminar Room 1\, Newton Institute
CONTACT:INI IT
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