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DTSTART:19700329T010000
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CATEGORIES:CCIMI Short Course: Tamara Broderick (MIT)
SUMMARY: Nonparametric Bayesian Methods: Models\, Algorith
ms\, and Applications (Lecture 3) - Tamara Broderi
ck (MIT)
DTSTART;TZID=Europe/London:20200117T153000
DTEND;TZID=Europe/London:20200117T170000
UID:TALK136291AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/136291
DESCRIPTION:Nonparametric Bayesian methods make use of infinit
e-dimensional mathematical structures to allow the
practitioner\nto learn more from their data as th
e size of their data set grows. What does that mea
n\, and how does it work in practice? In this tuto
rial\, we'll cover why machine learning and statis
tics need more than just parametric Bayesian infer
ence. We'll introduce such foundational nonparamet
ric Bayesian models as the Dirichlet process and C
hinese restaurant process and touch on the wide va
riety of models available in nonparametric Bayes.
Along the way\, we'll see what exactly nonparametr
ic Bayesian methods are and what they accomplish.
LOCATION:MR14 (FL.05)\, Pavilion F\, CMS
CONTACT:J.W.Stevens
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