EFFICIENT MESSAGE REPRESENTATIONS FOR BELIEF PROPAGATION ALGORITHMS
First Claim
1. A method for determining probabilities of states of a system represented by a model including a plurality of nodes connected by links, each node representing possible states of a corresponding part of the system, and each link representing statistical dependencies between possible states of related nodes, comprising:
- applying a belief propagation algorithm to estimate a minimum energy of the system defining belief propagation messages;
compressing the belief propagation messages; and
determining approximate probabilities of the states of the system from the compressed messages.
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Abstract
A method is provided for determining probabilities of states of a system represented by a model including a plurality of nodes connected by links. Each node represents possible states of a corresponding part of the system and each link represents statistical dependencies between possible states of related nodes. The method includes applying a belief propagation algorithm to estimate a minimum energy of the system defining belief propagation messages. The belief propagation messages are compressed and approximate probabilities of the states of the system are determined from the compressed messages.
22 Citations
21 Claims
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1. A method for determining probabilities of states of a system represented by a model including a plurality of nodes connected by links, each node representing possible states of a corresponding part of the system, and each link representing statistical dependencies between possible states of related nodes, comprising:
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applying a belief propagation algorithm to estimate a minimum energy of the system defining belief propagation messages; compressing the belief propagation messages; and determining approximate probabilities of the states of the system from the compressed messages. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A method for reducing intramessage redundancy in belief propagation messages, comprising:
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developing a plurality of belief propagation messages for a Markov network representation of a system; and compressing the belief propagation messages. - View Dependent Claims (20, 21)
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Specification