Friday, November 09, 2007

Ising model

Ising Models are important for Machine Learning because they are well-studied physical counter-parts of binary valued undirected graphical models. Belief Propagation in such models is equivalent to iteration of the Bethe-Pieirls fixed point equations. Recently Michael Chertkov and Vladimir Chernyak formulated an expression that gave exact expression for the partition function in terms of a local BP solution (slides), and Gómez,Mooij,Kappen followed up by truncating the exact expression and applying it to diagnostic inference task (related slides, approach based on method by Montanari and Rizzo)slides)

While catching up on Ising models, here are a few Ising Model introductory materials I've scanned/scavenged

2 comments:

Joris Mooij said...

The slides you linked to are actually about another method to correct approximate inference methods for the influence of loops, which is not directly related to the method proposed by Chertkov and Chernyak, but to the method proposed by Montanari and Rizzo.

Yaroslav said...

Thanks for the correction, I've added a note