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369
On the fixed points of the maxproduct algorithm
, 2000
"... Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The maxproduct "belief propagation" algorithm is a localmessage passing algorithm on this graph that is known to converge to a unique fixed point when the g ..."
Abstract

Cited by 12 (1 self)
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Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The maxproduct "belief propagation" algorithm is a localmessage passing algorithm on this graph that is known to converge to a unique fixed point when
Tree Consistency and Bounds on the Performance of the MaxProduct Algorithm and Its Generalizations
, 2002
"... Finding the maximum a posteriori (MAP) assignment of a discretestate distribution specified by a graphical model requires solving an integer program. The maxproduct algorithm, also known as the maxplus or minsum algorithm, is an iterative method for (approximately) solving such a problem on gr ..."
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Cited by 65 (5 self)
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Finding the maximum a posteriori (MAP) assignment of a discretestate distribution specified by a graphical model requires solving an integer program. The maxproduct algorithm, also known as the maxplus or minsum algorithm, is an iterative method for (approximately) solving such a problem
Graph Cuts is a MaxProduct Algorithm
"... The maximum a posteriori (MAP) configuration of binary variable models with submodular graphstructured energy functions can be found efficiently and exactly by graph cuts. Maxproduct belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexa ..."
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Cited by 6 (1 self)
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The maximum a posteriori (MAP) configuration of binary variable models with submodular graphstructured energy functions can be found efficiently and exactly by graph cuts. Maxproduct belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical
Interpreting Graph Cuts as a MaxProduct Algorithm
"... The maximum a posteriori (MAP) configuration of binary variable models with submodular graphstructured energy functions can be found efficiently and exactly by graph cuts. Maxproduct belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexa ..."
Abstract

Cited by 2 (2 self)
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The maximum a posteriori (MAP) configuration of binary variable models with submodular graphstructured energy functions can be found efficiently and exactly by graph cuts. Maxproduct belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical
MaxProduct Algorithms for the Generalized Multiple Fault Diagnosis Problem
"... Abstract—In this paper, we study the application of the maxproduct algorithm to the generalized multiple fault diagnosis (GMFD) problem. The GMFD is described by a set of components (or diseases), a set of alarms (or symptoms) and a set of causal dependencies between them. More specifically, given ..."
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Abstract—In this paper, we study the application of the maxproduct algorithm to the generalized multiple fault diagnosis (GMFD) problem. The GMFD is described by a set of components (or diseases), a set of alarms (or symptoms) and a set of causal dependencies between them. More specifically
MultitargetMultisensor Data Association Using the TreeReweighted MaxProduct Algorithm
 In SPIE Aerosense Conference
, 2003
"... Data association is a fundamental problem in multitargetmultisensor tracking. It entails selecting the most probable association between sensor measurements and target tracks from a very large set of possibilities. With N sensors and n targets in the detection range of each sensor, even with perfec ..."
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Cited by 14 (6 self)
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/minsum algorithm) can be applied. We use a treereweighted version of the usual maxproduct algorithm that either outputs the MAP data association, or acknowledges failure. For acyclic graphs, this messagepassing algorithm can solve the data association problem directly and recursively with complexity
Convex MaxProduct Algorithms for Continuous MRFs with Applications to Protein Folding
"... This paper investigates convex belief propagation algorithms for Markov random fields (MRFs) with continuous variables. Our first contribution is a theorem generalizing properties of the discrete case to the continuous case. Our second contribution is an algorithm for computing the value of the Lagr ..."
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Cited by 14 (4 self)
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of the Lagrangian relaxation of the MRF in the continuous case based on associating the continuous variables with an everfiner interval grid. A third contribution is a particle method which uses convex maxproduct in resampling particles. This last algorithm is shown to be particularly effective for protein
A Simpler MaxProduct Maximum Weight Matching Algorithm and the Auction Algorithm
 IEEE transactions on Information Theory
, 2008
"... Abstract — The maxproduct “belief propagation ” algorithm has received a lot of attention recently due to its spectacular success in many application areas such as iterative decoding, computer vision and combinatorial optimization. There is a lot of ongoing work investigating the theoretical proper ..."
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Cited by 7 (3 self)
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Abstract — The maxproduct “belief propagation ” algorithm has received a lot of attention recently due to its spectacular success in many application areas such as iterative decoding, computer vision and combinatorial optimization. There is a lot of ongoing work investigating the theoretical
On the optimality of treereweighted maxproduct message passing
 In UAI
, 2005
"... Treereweighted maxproduct (TRW) message passing [9] is a modified form of the ordinary maxproduct algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a config ..."
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Cited by 66 (5 self)
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Treereweighted maxproduct (TRW) message passing [9] is a modified form of the ordinary maxproduct algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a
Maximum weight matching via maxproduct belief propagation
 in International Symposium of Information Theory
, 2005
"... Abstract — The maxproduct “belief propagation ” algorithm is an iterative, local, message passing algorithm for finding the maximum a posteriori (MAP) assignment of a discrete probability distribution specified by a graphical model. Despite the spectacular success of the algorithm in many applicati ..."
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Cited by 63 (12 self)
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Abstract — The maxproduct “belief propagation ” algorithm is an iterative, local, message passing algorithm for finding the maximum a posteriori (MAP) assignment of a discrete probability distribution specified by a graphical model. Despite the spectacular success of the algorithm in many
Results 1  10
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369