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Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1791 (69 self)
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computational rule, the sumproduct algorithm operates in factor graphs to computeeither exactly or approximatelyvarious marginal functions by distributed messagepassing in the graph. A wide variety of algorithms developed in artificial intelligence, signal processing, and digital communications can
Convergence of the SumProduct Algorithm
 In Information Theory Workshop, 2003. Proceedings. 2003 IEEE
, 2003
"... Abstract  We address the question of convergence in the sumproduct algorithm. Specifically, we relate convergence of the sumproduct algorithm to the existence of a weak limit for a sequence of Gibbs measures defined on the associated computation tree. Using tools from the theory of Gibbs measures ..."
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Cited by 10 (0 self)
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Abstract  We address the question of convergence in the sumproduct algorithm. Specifically, we relate convergence of the sumproduct algorithm to the existence of a weak limit for a sequence of Gibbs measures defined on the associated computation tree. Using tools from the theory of Gibbs
The sumproduct algorithm on simple graphs
"... Abstract — This article summarizes work in progress on theoretical analysis of the sumproduct algorithm. Two families of graphs with quite different characteristics are studied: graphs in which all checks have degree two and graphs with a single cycle. Each family has a relatively simple structure ..."
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Abstract — This article summarizes work in progress on theoretical analysis of the sumproduct algorithm. Two families of graphs with quite different characteristics are studied: graphs in which all checks have degree two and graphs with a single cycle. Each family has a relatively simple structure
SumProduct Algorithm and Feedback Capacity
"... In this paper, we explore the link between the sumproduct algorithm and the feedback capacity of a channel with memory. We show that the optimal (i.e., capacityachieving) feedback is captured by the causal posterior state probabilities. For finitestate machine channels, the optimal feedback is ca ..."
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In this paper, we explore the link between the sumproduct algorithm and the feedback capacity of a channel with memory. We show that the optimal (i.e., capacityachieving) feedback is captured by the causal posterior state probabilities. For finitestate machine channels, the optimal feedback
Sub Graph Approach In Iterative SumProduct Algorithm
"... A new scheduling algorithm to the iterative sumproduct algorithm, which is called subgraph scheduling, will be presented in this paper. The propesed algorithm provides a schedule which has a higher convergence rate than the iterative sumproduct algorithm while keeping the complexity of one iterat ..."
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A new scheduling algorithm to the iterative sumproduct algorithm, which is called subgraph scheduling, will be presented in this paper. The propesed algorithm provides a schedule which has a higher convergence rate than the iterative sumproduct algorithm while keeping the complexity of one
Convergence analysis of reweighted sumproduct algorithms
 In Int. Conf. Acoustic, Speech and Sig. Proc
, 2007
"... Abstract—Markov random fields are designed to represent structured dependencies among large collections of random variables, and are wellsuited to capture the structure of realworld signals. Many fundamental tasks in signal processing (e.g., smoothing, denoising, segmentation etc.) require efficie ..."
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Cited by 13 (3 self)
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“messagepassing ” algorithms. This paper studies the convergence and stability properties of the family of reweighted sumproduct algorithms, a generalization of the widely used sumproduct or belief propagation algorithm, in which messages are adjusted with graphdependent weights. For pairwise Markov
Sufficient conditions for convergence of the sumproduct algorithm
 IEEE Trans. IT
, 2007
"... Abstract—Novel conditions are derived that guarantee convergence ..."
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Cited by 62 (2 self)
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Abstract—Novel conditions are derived that guarantee convergence
Efficient implementations of the sumproduct algorithm for decoding of LDPC codes
 in Proc. IEEE Globecom 2001
, 2001
"... Abstract — Efficient implementations of the sumproduct algorithm (SPA) for decoding lowdensity paritycheck (LDPC) codes using loglikelihood ratios (LLR) as messages between symbol and paritycheck nodes are presented. Various reducedcomplexity derivatives of the LLRSPA are proposed. Both seria ..."
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Cited by 74 (1 self)
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Abstract — Efficient implementations of the sumproduct algorithm (SPA) for decoding lowdensity paritycheck (LDPC) codes using loglikelihood ratios (LLR) as messages between symbol and paritycheck nodes are presented. Various reducedcomplexity derivatives of the LLRSPA are proposed. Both
A Hilbert Space of Probability Mass Functions and Applications on the SumProduct Algorithm
"... Abstract—In this paper a Hilbert space structure of probability mass functions (PMF) will be presented. The tools provided by the Hilbert space, specifically the norm and the inner product, may be useful while analyzing and improving the sumproduct algorithm in many aspects. Our approach provides a ..."
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Cited by 1 (0 self)
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Abstract—In this paper a Hilbert space structure of probability mass functions (PMF) will be presented. The tools provided by the Hilbert space, specifically the norm and the inner product, may be useful while analyzing and improving the sumproduct algorithm in many aspects. Our approach provides
Belief Propagation Estimation of Protein and Domain Interactions using the SumProduct Algorithm
, 2009
"... We present a novel framework to estimate proteinprotein (PPI) and domaindomain (DDI) interactions based on a belief propagation estimation method that efficiently computes interaction probabilities. This methodology uses experimental interactions, domain architecture and GO annotations to create a ..."
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is then efficiently marginalized with a message passing algorithm called the SumProduct Algorithm (SPA). This method is compared against two other approaches: Maximum Likelihood Estimation and Maximum Specificity Set Cover. SPA performs better for simulated scenarios and for inferring high quality PPI data
Results 1  10
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