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SUMMARY:Nonparametric Bayesian Methods: Models\, Algorithms\, and Applicat
 ions (Lecture 1) - Tamara Broderick (MIT)
DTSTART:20200115T160000Z
DTEND:20200115T170000Z
UID:TALK136360@talks.cam.ac.uk
CONTACT:J.W.Stevens
DESCRIPTION:Nonparametric Bayesian methods make use of infinite-dimensiona
 l mathematical structures to allow the practitioner to learn more from the
 ir data as the size of their data set grows. What does that mean\, and how
  does it work in practice? In this tutorial\, we’ll cover why machine le
 arning and statistics need more than just parametric Bayesian inference. W
 e’ll introduce such foundational nonparametric Bayesian models as the Di
 richlet process and Chinese restaurant process and touch on the wide varie
 ty of models available in nonparametric Bayes. Along the way\, we’ll see
  what exactly nonparametric Bayesian methods are and what they accomplish.
 \n
LOCATION:MR15 (GL.02)\, Pavilion G\, CMSn F\, CMS
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