Gauges, Loops, and Polynomials for Partition Functions of Graphical Models

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" summary

Describes using gauge transformations and loop calculus to relate partition functions of graphical models to weighted graph polynomials; explains how to express partition functions as sums over generalized loops and how polynomial representations capture contributions from beliefs or gauges, with discussion of algorithmic and structural consequences for counting and inference on graphs.

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" chapters (6)

1. 0:00Start
2. 5:27Exact Variational Principe for Partition Function
3. 7:14Bethe- (BP-) Variational Principe for Partition Function
4. 11:48Graphical Model Examples: Hard- & Soft
5. 14:25Outline
6. 23:43Partition Function via Graph-R = Elimination of Edges