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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 ..."
Abstract

Cited by 1791 (69 self)
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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
An Introduction to Factor Graphs
 IEEE SIGNAL PROCESSING MAG., JAN. 2004
, 2004
"... A large variety of algorithms in coding, signal processing, and artificial intelligence may be viewed as instances of the summaryproduct algorithm (or belief/probability ..."
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Cited by 197 (34 self)
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A large variety of algorithms in coding, signal processing, and artificial intelligence may be viewed as instances of the summaryproduct algorithm (or belief/probability
Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms
 IEEE Transactions on Information Theory
, 2005
"... Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems t ..."
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Cited by 585 (13 self)
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Important inference problems in statistical physics, computer vision, errorcorrecting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems
Kalman filters, factor graphs, and . . .
, 2002
"... This postdiploma project highlights connections between Kalman filters, factor graphs, and ..."
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This postdiploma project highlights connections between Kalman filters, factor graphs, and
On factor graphs and the Fourier transform
 IN IEEE TRANS. INFORM. THEORY
, 2005
"... We introduce the concept of convolutional factor graphs, which represent convolutional factorizations of multivariate functions, just as conventional (multiplicative) factor graphs represent multiplicative factorizations. Convolutional and multiplicative factor graphs arise as natural Fourier trans ..."
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Cited by 17 (4 self)
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We introduce the concept of convolutional factor graphs, which represent convolutional factorizations of multivariate functions, just as conventional (multiplicative) factor graphs represent multiplicative factorizations. Convolutional and multiplicative factor graphs arise as natural Fourier
Some Remarks on Factor Graphs
 Proc. 3rd Int. Symp. on Turbo Codes and Related Topics
, 2003
"... The paper is a collection of remarks, some rather plain, on various issues with factor graphs. In particular, it is pointed out that powerful signal processing techniques such as gradient methods, Kalman filtering, and the particle filter can be used and combined in factor graphs. ..."
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Cited by 15 (6 self)
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The paper is a collection of remarks, some rather plain, on various issues with factor graphs. In particular, it is pointed out that powerful signal processing techniques such as gradient methods, Kalman filtering, and the particle filter can be used and combined in factor graphs.
Factor graphs and algorithms
 IN PROC. 35TH ALLERTON CONF. COMMUNICATIONS, CONTROL, AND COMPUTING
"... A factor graph is a bipartite graph that expresses how a global function of several variables factors into a product of local functions. Factor graphs subsume many other graphical models, including Bayesian networks, Markov random fields, and Tanner graphs. We describe a general algorithm for comput ..."
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Cited by 36 (7 self)
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A factor graph is a bipartite graph that expresses how a global function of several variables factors into a product of local functions. Factor graphs subsume many other graphical models, including Bayesian networks, Markov random fields, and Tanner graphs. We describe a general algorithm
Normal Factor Graphs
, 2014
"... This thesis introduces normal factor graphs under a new semantics, namely, the exterior function semantics. Initially, this work was motivated by two distinct lines of research. One line is “holographic algorithms,” a powerful approach introduced by Valiant for solving various counting problems in ..."
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This thesis introduces normal factor graphs under a new semantics, namely, the exterior function semantics. Initially, this work was motivated by two distinct lines of research. One line is “holographic algorithms,” a powerful approach introduced by Valiant for solving various counting problems
On Factor Graphs And Electrical Networks
 in Mathematical Systems Theory in Biology, Communication, Computation, and Finance, IMA Volumes in Math
, 2002
"... Factor graphs are graphical models with origins in coding theory. The sumproduct and the maxproduct algorithms, which operate by message passing in a factor graph, subsume a great variety of algorithms in coding, signal processing, and artificial intelligence. In this paper, factor graphs are used ..."
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Cited by 13 (7 self)
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Factor graphs are graphical models with origins in coding theory. The sumproduct and the maxproduct algorithms, which operate by message passing in a factor graph, subsume a great variety of algorithms in coding, signal processing, and artificial intelligence. In this paper, factor graphs
FactorGraph Algorithms for Equalization
, 2007
"... In this paper, we use the factorgraph framework to describe the statistical relationships that arise in the equalization of data transmitted over an intersymbol interference channel, and use it to develop several new algorithms for linear and decision feedback approaches. Specifically, we examine b ..."
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Cited by 9 (1 self)
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In this paper, we use the factorgraph framework to describe the statistical relationships that arise in the equalization of data transmitted over an intersymbol interference channel, and use it to develop several new algorithms for linear and decision feedback approaches. Specifically, we examine
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