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BEGIN:VEVENT
DTSTAMP:20260808T140224Z
DTSTART;VALUE=DATE-TIME:20170505T140000
DTEND;VALUE=DATE-TIME:20170505T150000
SUMMARY:CRiSM Seminar
TZID:Europe/London
UID:20170505-094d4345576c31f20157bd3a3a3f01d6@warwick.ac.uk
CREATED:20161013T084812Z
DESCRIPTION:"Adaptive MCMC For Everyone" Jeffrey Rosenthal\, University o
 f Toronto Markov chain Monte Carlo (MCMC) algorithms\, such as the Metro
 polis Algorithm and the Gibbs Sampler\, are an extremely useful and popu
 lar method of approximately sampling from complicated probability distri
 butions. Adaptive MCMC attempts to automatically modify the algorithm wh
 ile it runs\, to improve its performance on the fly. However\, such adap
 tation often destroys the ergodicity properties necessary for the algori
 thm to be valid. In this talk\, we first illustrate MCMC algorithms usin
 g simple graphical Java applets. We then discuss adaptive MCMC\, and pre
 sent examples and theorems concerning its ergodicity and efficiency. We 
 close with some recent ideas which make adaptive MCMC more widely applic
 able in broader contexts.
LOCATION:D1.07
URL:http://www2.warwick.ac.uk/fac/sci/statistics/crism/seminars/
ATTACH:http://www2.warwick.ac.uk/fac/sci/statistics/crism/seminars/
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20170424T084253Z
ORGANIZER;CN=David Kinmond:
END:VEVENT
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