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SUMMARY: Nonparametric Bayesian Methods: Models\, Algorithms\, and Applica
 tions (Lecture 3) - Tamara Broderick (MIT)
DTSTART:20200117T153000Z
DTEND:20200117T170000Z
UID:TALK136291@talks.cam.ac.uk
CONTACT:J.W.Stevens
DESCRIPTION:Nonparametric Bayesian methods make use of infinite-dimensiona
 l mathematical structures to allow the practitioner\nto learn more from th
 eir data as the size of their data set grows. What does that mean\, and ho
 w does it work in practice? In this tutorial\, we'll cover why machine lea
 rning and statistics need more than just parametric Bayesian inference. We
 'll introduce such foundational nonparametric Bayesian models as the Diric
 hlet process and Chinese restaurant process and touch on the wide variety 
 of models available in nonparametric Bayes. Along the way\, we'll see what
  exactly nonparametric Bayesian methods are and what they accomplish.
LOCATION:MR14 (FL.05)\, Pavilion F\, CMS
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