Properties of BP Algorithm

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Presentation transcript:

Properties of BP Algorithm Inference Probabilistic Graphical Models Message Passing Properties of BP Algorithm

Calibration Cluster beliefs: A cluster graph is calibrated if every pair of adjacent clusters Ci,Cj agree on their sepset Si,j 2: B,C 1: A,B 3: C,D 4: A,D C A B D

Convergence  Calibration

Reparameterization Sepset marginals: 2: B,C 1: A,B 3: C,D 4: A,D C A B

Reparameterization Sepset marginals:

Summary At convergence of BP, cluster graph beliefs are calibrated: beliefs at adjacent clusters agree on sepsets Cluster graph beliefs are an alternative, calibrated parameterization of the original unnormalized density No information is lost by message passing

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