Join-Graph Propagation Algorithms

by   Robert Mateescu, et al.

The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl's belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. Algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of iterative BP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.


page 1

page 2

page 3

page 4


Iterative Join-Graph Propagation

The paper presents an iterative version of join-tree clustering that app...

Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation

The analysis of Belief Propagation and other algorithms for the reconst...

Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding

It was recently shown that the problem of decoding messages transmitted ...

Deep learning via message passing algorithms based on belief propagation

Message-passing algorithms based on the Belief Propagation (BP) equation...

Lifted Region-Based Belief Propagation

Due to the intractable nature of exact lifted inference, research has re...

Structured Message Passing

In this paper, we present structured message passing (SMP), a unifying f...

Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference

We design iterative receiver schemes for a generic wireless communicatio...