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CATEGORIES:Statistics Reading Group
SUMMARY:Bayesian Nonparametric Mixture Models - Jurgen Van
  Gael\, Engineering department\, University of Cam
 bridge
DTSTART;TZID=Europe/London:20091028T163000
DTEND;TZID=Europe/London:20091028T180000
UID:TALK20909AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/20909
DESCRIPTION:Although Bayesian nonparametric models have been a
 round for a while\, recent advances in theory and 
 computational methods have led to exciting new app
 lications of this family of techniques. As a start
 ing point into Bayesian nonparametrics\, we will l
 ook at Bayesian nonparametric mixture models: why 
 they are used and how they are used.\n\nThe Dirich
 let process will be the basic building block for t
 he mixture models which we discuss. We will discus
 s different - equivalent - representations of the 
 Dirichlet process and explain how we can build a m
 ixture model using them. We will touch on how to d
 o inference in these models and show some example 
 applications.\n\nThe discussion will not be going 
 through one paper in particular but a very readabl
 e paper that touches on many issues which we will 
 discuss is: "Bayesian Density Estimation and Infer
 ence Using Mixtures":http://www.questia.com/google
 Scholar.qst?docId=5002233859\, Michael D. Escobar\
 , Mike West\; Journal of the American Statistical 
 Association\, Vol. 90\, 1995\n
LOCATION:MR5\, CMS
CONTACT:Richard Samworth
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