Dirichlet Processes, Chinese Restaurant Processes, and all that

author: Michael I. Jordan, Department of Electrical Engineering and Computer Sciences, UC Berkeley
published: Feb. 25, 2007,   recorded: August 2005,   views: 14878
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Description

Bayesian approaches to learning problems have many virtues, including their ability to make use of prior knowledge and their ability to link related sources of information, but they also have many vices, notably the strong parametric assumptions that are often invoked willy-nilly in practical Bayesian modeling. Nonparametric Bayesian methods offer a way to make use of the Bayesian calculus without the parametric handcuffs. In this talk I describe several recent explorations in nonparametric Bayesian modeling and inference, including various versions of "Chinese restaurant process priors" that allow flexible structures to be learned and allow sharing of statistical strength among sets of related structures. I discuss applications to problems in bioinformatics and information retrieval.

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Reviews and comments:

Comment1 Andrew Dai, July 30, 2007 at 6:07 p.m.:

The wmv video seems to get stuck at 40:12 with no way of seeing the rest of it.


Comment2 4MD, December 24, 2007 at 11:40 p.m.:

Pls convert video to flash


Comment3 xyqian@ecust.edu.cn, January 12, 2008 at 3:01 p.m.:

Can I download the video?


Comment4 daniel, May 29, 2008 at 5:31 p.m.:

I'm not able to see the video at all!


Comment5 xiaodong, February 24, 2009 at 7:59 p.m.:

why I can not download the PDF file of the slides? The image version of the slides is too blur.


Comment6 Max Dama, March 2, 2009 at 9:07 p.m.:

This is the most lucid videolectures.net lecture on Dirichlet nonparametric bayes. Michael is a great speaker.


Comment7 simon, February 28, 2012 at 11:37 p.m.:

talks way too fast...sucks ass

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