Generalized Belief Propagation

In 2002, I gave a lecture at the Mathematical Sciences Research Institute on the work I did, together with Bill Freeman and Yair Weiss on Generalized Belief Propagation, and the correspondence between free energy approximations and message passing algorithms. The lecture is available as a streaming video, together with a pdf for the slides, here.

It’s worth mentioning that there are many other interesting research lectures available in MSRI’s video archive, and that the more recent ones are of higher production quality.

Here is our most recent and comprehensive paper on this subject, published in the July 2005 issue of IEEE Transactions on Information Theory, which gives many additional details compared to the lecture: MERL TR2004-040.

If that paper is too difficult, you should probably start with this earlier paper, which was more tutorial in nature: MERL TR2001-22.

If you’re looking for generalized belief propagation software, your best bet is this package written by Yair’s student Talya Meltzer.

P.S.: I realized I haven’t told those of you who don’t know anything about it what generalized belief propagation is. Well, one answer is to that is look at the above material! But here’s a little background text that I’ve copied from my research statement to explain why you might be interested:

Most of my current research involves the application of statistical methods to “inference” problems. Some important fields which are dominated by the issue of inference are computer vision, speech recognition, natural language processing, error-control coding and digital communications. Essentially, any time you are receiving a noisy signal, and need to infer what is really out there, you are dealing with an inference problem.

A productive way to deal with an inference problem is to formalize it as a problem of computing probabilities in a “graphical model.” Graphical models, which are referred to in various guises as “Markov random fields,” “Bayesian networks,” or “factor graphs,” provide a statistical framework to encapsulate our knowledge of a system and to infer from incomplete information.

Physicists who use the techniques of statistical mechanics to study the behavior of disordered magnetic spin systems are actually studying a mathematically equivalent problem to the inference problem studied by computer scientists or electrical engineers, but with different terminology, goals, and perspectives. My own research has focused on the surprising relationships between methods that are used in these communities, and on powerful new techniques and algorithms, such as Generalized Belief Propagation, that can be understood using those relationships.

I’ll tell you more in future posts; I promise.

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3 Responses to “Generalized Belief Propagation”

  1. Lectures on Disordered Systems « Nerd Wisdom Says:

    […] systems. Those methods have turned out to be closely connected to approaches, such as the “belief propagation” algorithm, that are widely used in computer science, artificial intelliegence, and communciations theory, […]

  2. Computing Free Energies « Nerd Wisdom Says:

    […] is that you can make systematic corrections; belief propagation can also be corrected with generalized belief propagation, but the procedure is less automatic. Disadvantages include the fact that the free energy is only […]

  3. Jiang Says:

    I am interested in looking at the video, but the MSRI archive link is no longer valid, any other way to retrieve the lecture?

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