This article introduces a class of discrete iterative algorithms that are provably convergent alternatives to belief propagation (BP) and generalized belief propagation (GBP). Our work builds on recent results by Yedidia, Freeman, and Weiss (2000), who showed that the fixed points of BP and GBP algorithms correspond to extrema of the Bethe and Kikuchi free energies, respectively. We obtain two ...

Belief propagation (BP) is a message-passing heuristic for statistical inference in graphical models such as Bayesian networks and Markov random fields. BP is used to compute marginal distributions or maximum likelihood assignments and has applications in many areas, including machine learning, image processing, and computer vision. However, the theoretical understanding of the performance of B...

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label the class of a node in a network based on its neighbors and appropriate assumptions of homophily (“birds of a feather flock together”) or heterophily (“opposites attract”). One of the most widely u...

We propose a hybrid message passing method for distributed cooperative localization and tracking of mobile agents. Belief propagation (BP) and mean field (MF) message passing are employed for, respectively, the motion-related and measurement-related parts of the underlying factor graph. Using a Gaussian belief approximation, closed-form expressions of all messages are obtained, and only three r...

Freehand sketches are complex for recognition. Individual fragments of the drawing are often ambiguous to be interpreted without contextual cues. Markov Random Field (MRF) that ends up with a global model by simply specifying local interactions can naturally suffice the requirement. In our project, a recognizer based on MRF has been constructed to jointly analyze local features in order to inco...

We formulate a Belief Propagation (BP) algorithm in the context of the capacitated minimum-cost network flow problem (MCF). Unlike most of the instances of BP studied in the past, the messages of BP in the context of this problem are piecewise-linear functions. We prove that BP converges to the optimal solution in pseudo-polynomial time, provided that the optimal solution is unique and the prob...

In this paper, reliability based decoding is combined with belief propagation (BP) decoding for low-density parity check (LDPC) codes. At each iteration, the soft output values delivered by the BP algorithm are used as reliability values to perform reduced complexity soft decision decoding of the code considered. This approach allows to bridge the error performance gap between belief propagatio...

A wide range of low level vision problems have been formulated in terms of finding the most probable assignment of a Markov Random Field (or equivalently the lowest energy configuration). Perhaps the most successful example is in the case of stereo vision. For the stereo problem, it has been shown that finding the global optimum is NP hard but good results have been obtained using a number of a...

In this paper, we focus on diagnosis in distributed computer systems using end-to-end transactions, or probes. Diagnostic problem is formulated as a probabilistic inference in a bipartite noisy-OR Bayesian network. Due to general intractability of exact inference in such networks, we apply belief propagation (BP), a popular approximation technique proven successful in various applications, from...

Bayesian inference is often hampered by large computational expense. As a generalization of belief propagation (BP), expectation propagation (EP) approximates exact Bayesian computation with efficient message passing updates. However, when an approximation family used by EP is far from exact posterior distributions, message passing may lead to poor approximation quality and suffer from divergen...