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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 2) - Tamara Broderic
k (MIT)
DTSTART;TZID=Europe/London:20200116T153000
DTEND;TZID=Europe/London:20200116T170000
UID:TALK136288AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/136288
DESCRIPTION:Nonparametric Bayesian methods make use of\ninfini
te-dimensional mathematical structures to allow th
e practitioner to 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\nand
Chinese restaurant process and touch on the wide v
ariety of models available in nonparametric Bayes.
Along the way\, we'll see what exactly nonparamet
ric Bayesian methods are and what they accomplish.
LOCATION:MR11 (B1.39)\, Pavilion B\, CMS
CONTACT:J.W.Stevens
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