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In the context of online clustering, I often find many papers talking about: "dirichlet process" and "finite/infinite mixture models".

Given that I've never used or read about dirichlet process or mixture models. Do you know any suggestions of introductory lectures or papers that are easy to understand, about that ?

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Is your background in statistics or in computer science? –  MånsT Jun 15 '12 at 11:44
    
@MånsT I know about classical probability theory and the usual distributions (gaussian etc), I've already designed and implemented some online clustering algorithms, and wanted recently to make use of particle filters to do some online clustering, but each time I find papers talking about DP and Mixture models, so I want to know about that. –  shn Jun 15 '12 at 11:54
    
I like Finite mixture models by McLachlan and Peel. I haven't really got any recommendations for DP though. –  MånsT Jun 15 '12 at 11:58
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3 Answers

This is a gentle tutorial:

http://www.cs.cmu.edu/~kbe/dp_tutorial.pdf

I like the wikipedia entry as well. The links there are also very good:

http://en.wikipedia.org/wiki/Dirichlet_process

Here is a summer school lecture of one of the most active researchers

http://videolectures.net/mlss07_teh_dp/

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Another possibility is Introduction to the Dirichlet Distribution and Related Processes, but I'm afraid I haven't read it yet, however it is down for our next reading group!

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Looks promising (if long)! –  Stumpy Joe Pete Nov 29 '12 at 19:40
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Here's a rather good introductory video lecture by Tom Griffiths, also given at the Machine Learning Summer School.

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