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SUMMARY:An introduction to Bayesian nonparametrics: some inference schemes
  for infinite mixture models - Maria Lomeli
DTSTART:20160505T133000Z
DTEND:20160505T150000Z
UID:TALK65404@talks.cam.ac.uk
CONTACT:Yingzhen Li
DESCRIPTION:An introduction to this exciting research area will be given\,
  focusing on the two basic (and famous) processes: Dirichlet and Pitman-Yo
 r (or Chinese restaurant and two parameter Chinese restaurant\, respective
 ly\, for their corresponding exchangeable random partition representations
 ). These two processes will be used as building blocks of an infinite mixt
 ure model. Successively\, I will review how their different constructions/
 representations are useful to construct different algorithms. Then\, I wil
 l proceed to talk about the two Markov Chain Monte Carlo schemes in detail
 . Finally\, if time permits\, I talk about a novel MCMC scheme that exploi
 ts the advantages of the two existing MCMC schemes (Lomeli et al\, 2015).\
 n\n\nReferences: (no pre-reading required but these are very useful in gen
 eral)\n\n"Yee Whye's tutorial":http://mlg.eng.cam.ac.uk/tutorials/07/ywt.p
 df\n\n"Zoubin's tutorial":http://mlg.eng.cam.ac.uk/zoubin/talks/uai05tutor
 ial-b.pdf\n\n"Peter Orbanz webpage":http://stat.columbia.edu/%7Eporbanz/np
 b-tutorial.html\n(very thorough list of references therein)\n\n"Lomeli et 
 al (2015)":https://papers.nips.cc/paper/5799-a-hybrid-sampler-for-poisson-
 kingman-mixture-models \n(for the last part of the talk)\n\n
LOCATION:Engineering Department\, CBL Room 438
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