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DTSTART:19700329T010000
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CATEGORIES:CCIMI Short Course: Tamara Broderick (MIT)
SUMMARY:Nonparametric Bayesian Methods: Models\, Algorithm
s\, and Applications (Lecture 1) - Tamara Broderic
k (MIT)
DTSTART;TZID=Europe/London:20200115T160000
DTEND;TZID=Europe/London:20200115T170000
UID:TALK136360AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/136360
DESCRIPTION:Nonparametric Bayesian methods make use of infinit
e-dimensional mathematical structures to allow the
practitioner to learn more from their data as the
size of their data set grows. What does that mean
\, and how does it work in practice? In this tutor
ial\, we’ll cover why machine learning and statist
ics need more than just parametric Bayesian infere
nce. We’ll introduce such foundational nonparametr
ic Bayesian models as the Dirichlet process and Ch
inese restaurant process and touch on the wide var
iety of models available in nonparametric Bayes. A
long the way\, we’ll see what exactly nonparametri
c Bayesian methods are and what they accomplish.\n
LOCATION:MR15 (GL.02)\, Pavilion G\, CMSn F\, CMS
CONTACT:J.W.Stevens
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