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SUMMARY:Foundations of Nonparametric Bayesian Methods (Part II) - Peter Or
 banz (University of Cambridge)
DTSTART:20081016T150000Z
DTEND:20081016T170000Z
UID:TALK14247@talks.cam.ac.uk
CONTACT:Peter Orbanz
DESCRIPTION:This 3-part tutorial will address a machine learning audience\
 , not\nassumed to be familiar with measure theory or the theory stochastic
 \nprocesses. The course is intended to provide (1) an overview of what\nno
 nparametric Bayesian models exist beyond those already used in\nmachine le
 arning\, and (2) a basic understanding of the mathematical\nconstruction o
 f ''process'' models\, both existing ones and new models\non a variety of 
 possible domains.\n\nPart II: Models on the simplex\n\nMost of the existin
 g Bayesian nonparametric literature\, especially in\nstatistics\, focusses
  on models on the simplex\, i.e. probabilities on\nprobabilities. This sec
 ond part will discuss different classes of\nexisting models and their prop
 erties\, including Dirichlet\, tailfree\nand Levy processes.\n\nWebpage:\n
 http://mlg.eng.cam.ac.uk/porbanz/npb-tutorial.html
LOCATION:Engineering Department\, CBL Room 438
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