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Belief Propagation for Structured Decision Making
[article]
2012
arXiv
pre-print
Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational
arXiv:1210.4897v1
fatcat:tpb6bigdxjh2bm5q4472or65mu