Results 1 - 10 of 369

On the fixed points of the max-product algorithm

by William T. Freeman, Yair Weiss , 2000
"... Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The max-product "belief propagation" algorithm is a local-message passing algorithm on this graph that is known to converge to a unique fixed point when the g ..."
Abstract - Cited by 12 (1 self) - Add to MetaCart
Graphical models, such as Bayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The max-product "belief propagation" algorithm is a local-message 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 Max-Product Algorithm and Its Generalizations

by Martin Wainwright, Tommi Jaakkola, Alan Willsky , 2002
"... Finding the maximum a posteriori (MAP) assignment of a discrete-state distribution specified by a graphical model requires solving an integer program. The max-product algorithm, also known as the max-plus or min-sum algorithm, is an iterative method for (approximately) solving such a problem on gr ..."
Abstract - Cited by 65 (5 self) - Add to MetaCart
Finding the maximum a posteriori (MAP) assignment of a discrete-state distribution specified by a graphical model requires solving an integer program. The max-product algorithm, also known as the max-plus or min-sum algorithm, is an iterative method for (approximately) solving such a problem

Graph Cuts is a Max-Product Algorithm

by Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
"... The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical counterexa ..."
Abstract - Cited by 6 (1 self) - Add to MetaCart
The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical

Interpreting Graph Cuts as a Max-Product Algorithm

by Daniel Tarlow, Inmar E. Givoni, Richard S. Zemel, Brendan J. Frey
"... The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product 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) - Add to MetaCart
The maximum a posteriori (MAP) configuration of binary variable models with submodular graph-structured energy functions can be found efficiently and exactly by graph cuts. Max-product belief propagation (MP) has been shown to be suboptimal on this class of energy functions by a canonical

Max-Product Algorithms for the Generalized Multiple Fault Diagnosis Problem

by Tung Le, Christoforos N. Hadjicostis
"... Abstract—In this paper, we study the application of the max-product algorithm to the generalized multiple fault diagnosis (GMFD) problem. The GMFD is described by a set of com-ponents (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 max-product algorithm to the generalized multiple fault diagnosis (GMFD) problem. The GMFD is described by a set of com-ponents (or diseases), a set of alarms (or symptoms) and a set of causal dependencies between them. More specifically

Multitarget-Multisensor Data Association Using the Tree-Reweighted Max-Product Algorithm

by Lei Chen, Martin J. Wainwright, Mujdat Cetin, Alan S. Willsky - In SPIE Aerosense Conference , 2003
"... Data association is a fundamental problem in multitarget-multisensor 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 ..."
Abstract - Cited by 14 (6 self) - Add to MetaCart
/min-sum algorithm) can be applied. We use a tree-reweighted version of the usual max-product algorithm that either outputs the MAP data association, or acknowledges failure. For acyclic graphs, this message-passing algorithm can solve the data association problem directly and recursively with complexity

Convex Max-Product Algorithms for Continuous MRFs with Applications to Protein Folding

by Jian Peng, Tamir Hazan, David Mcallester, Raquel Urtasun
"... 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 ..."
Abstract - Cited by 14 (4 self) - Add to MetaCart
of the Lagrangian relaxation of the MRF in the continuous case based on associating the continuous variables with an ever-finer interval grid. A third contribution is a particle method which uses convex max-product in re-sampling particles. This last algorithm is shown to be particularly effective for protein

A Simpler Max-Product Maximum Weight Matching Algorithm and the Auction Algorithm

by Mohsen Bayati, Devavrat Shah, Mayank Sharma - IEEE transactions on Information Theory , 2008
"... Abstract — The max-product “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 ..."
Abstract - Cited by 7 (3 self) - Add to MetaCart
Abstract — The max-product “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 tree-reweighted max-product message passing

by Vladimir Kolmogorov - In UAI , 2005
"... Tree-reweighted max-product (TRW) message passing [9] is a modified form of the ordinary max-product 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 ..."
Abstract - Cited by 66 (5 self) - Add to MetaCart
Tree-reweighted max-product (TRW) message passing [9] is a modified form of the ordinary max-product 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 max-product belief propagation

by Mohsen Bayati, Devavrat Shah, Mayank Sharma - in International Symposium of Information Theory , 2005
"... Abstract — The max-product “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 ..."
Abstract - Cited by 63 (12 self) - Add to MetaCart
Abstract — The max-product “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 of 369