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SUMMARY:Locally adaptive Monte Carlo methods - Lee\, A (University of Warw
 ick)
DTSTART:20140422T145000Z
DTEND:20140422T152500Z
UID:TALK52083@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Christophe Andrieu (University of Bristol)\, Arnau
 d Doucet (University of Oxford) \n\nIn various situations of interest\, na
 tural implementations of Monte Carlo algorithms such as Markov chain Monte
  Carlo and sequential Monte Carlo can perform poorly due to uneven perform
 ance in different parts of the space in which they operate. For example\, 
 in Markov chain Monte Carlo a Markov kernel may behave increasingly poorly
  in the tails of the target distribution of interest and in sequential Mon
 te Carlo the quality of associated estimates may plummet if too few partic
 les are used at a particular time. We overview a particular strategy\, loc
 al adaptation\, that seeks to overcome some of these phenomena in practice
 .\n
LOCATION:Seminar Room 1\, Newton Institute
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