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SSG Seminar Abstract


Walk-Sum Analysis and Interpretation of Gaussian Belief Propagation

Dmitry Malioutov
SSG, LIDS, MIT


We describe a powerful new framework based on walks in a graph for analysis and inference in Gaussian graphical models. The key idea is to decompose correlations between variables as an infinite sum over all walks between those variables in the graph. The weight of each walk is given by a product of edgewise partial correlation coefficients. We provide a walk-sum interpretation of Gaussian belief propagation in trees and of the approximate method of loopy belief propagation in graphs with cycles. This perspective leads to a better understanding of Gaussian belief propagation and to stronger results for its convergence in loopy graphs.


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