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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 computational rule, t ..."
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Cited by 1163 (67 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 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 be derived as specific instances of the sumproduct algorithm, including the forward/backward algorithm, the Viterbi algorithm, the iterative "turbo" decoding algorithm, Pearl's belief propagation algorithm for Bayesian networks, the Kalman filter, and certain fast Fourier transform algorithms.
Turbo decoding as an instance of Pearl’s belief propagation algorithm
 IEEE Journal on Selected Areas in Communications
, 1998
"... Abstract—In this paper, we will describe the close connection between the now celebrated iterative turbo decoding algorithm of Berrou et al. and an algorithm that has been well known in the artificial intelligence community for a decade, but which is relatively unknown to information theorists: Pear ..."
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Cited by 309 (15 self)
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Abstract—In this paper, we will describe the close connection between the now celebrated iterative turbo decoding algorithm of Berrou et al. and an algorithm that has been well known in the artificial intelligence community for a decade, but which is relatively unknown to information theorists: Pearl’s belief propagation algorithm. We shall see that if Pearl’s algorithm is applied to the “belief network ” of a parallel concatenation of two or more codes, the turbo decoding algorithm immediately results. Unfortunately, however, this belief diagram has loops, and Pearl only proved that his algorithm works when there are no loops, so an explanation of the excellent experimental performance of turbo decoding is still lacking. However, we shall also show that Pearl’s algorithm can be used to routinely derive previously known iterative, but suboptimal, decoding algorithms for a number of other errorcontrol systems, including Gallager’s
Iterative Decoding of Compound Codes by Probability Propagation in Graphical Models
 IEEE J. Sel. Areas Comm
, 1998
"... Abstract—We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms. After reviewing a variety of graphical models (Markov random fields, Tanner graphs, and Bayesian networks), we derive a general distributed marginalization algorithm for ..."
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Cited by 108 (12 self)
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Abstract—We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms. After reviewing a variety of graphical models (Markov random fields, Tanner graphs, and Bayesian networks), we derive a general distributed marginalization algorithm for functions described by factor graphs. From this general algorithm, Pearl’s belief propagation algorithm is easily derived as a special case. We point out that recently developed iterative decoding algorithms for various codes, including “turbo decoding ” of parallelconcatenated convolutional codes, may be viewed as probability propagation in a graphical model of the code. We focus on Bayesian network descriptions of codes, which give a natural input/state/output/channel description of a code and channel, and we indicate how iterative decoders can be developed for paralleland serially concatenated coding systems, product codes, and lowdensity paritycheck codes. Index Terms — Concatenated coding, decoding, graph theory, iterative methods, product codes.
Which Codes Have CycleFree Tanner Graphs?
 IEEE TRANS. INFORM. THEORY
, 1999
"... If a linear block code of length has a Tanner graph without cycles, then maximumlikelihood softdecision decoding of can be achieved in time O(n ). However, we show that cyclefree Tanner graphs cannot support good codes. Specifically, let be an (n; k; d) linear code of rate R = k=n that can ..."
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Cited by 33 (1 self)
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If a linear block code of length has a Tanner graph without cycles, then maximumlikelihood softdecision decoding of can be achieved in time O(n ). However, we show that cyclefree Tanner graphs cannot support good codes. Specifically, let be an (n; k; d) linear code of rate R = k=n that can be represented by a Tanner graph without cycles. We prove that if R 0:5 then d 2, while if R!0:5 then is obtained from a code of rate 0:5 and distance 2 by simply repeating certain symbols. In the latter case, we prove that k +1 ! R : Furthermore, we show by means of an explicit construction that this bound is tight for all values of n and k. We also prove that binary codes which have cyclefree Tanner graphs belong to the class of graphtheoretic codes, known as cutset codes of a graph. Finally, we discuss the asymptotics for Tanner graphs with cycles, and present a number of open problems for future research.
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 28 (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 for computing "marginals" of the global function by distributed messagepassing in the corresponding factor graph. A wide variety of algorithms developed in the artificial intelligence, statistics, signal processing, and digital communications communities can be derived as specific instances of this general algorithm, including Pearl's "belief propagation" and "belief revision" algorithms, the fast Fourier transform, the Viterbi algorithm, the forward/backward algorithm, and the iterative "turbo" decoding algorithm.