We present a new framework based on walks in a graph for analysis and inference in Gaussian graphical models. The key idea is to decompose the correlation between each pair of var...
Dmitry M. Malioutov, Jason K. Johnson, Alan S. Wil...
Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, bel...
In this paper, we consider the problem of stereo matching using loopy belief propagation. Unlike previous methods which focus on the original spatial resolution, we hierarchically...
Abstract –In this paper, a new problem, consensus estimation, is formulated, whose setting is complementary to the well-known CEO problem. In particular, a set of nodes are emplo...
Belief propagation is widely used in inference of graphical models. It yields exact solutions when the underlying graph is singly connected. When the graph contains loops, double-c...