Name
Abstract | ||
|---|---|---|
For undirected graphical models, belief propagation often performs remarkably well for approximate marginal inference, and may be viewed as a heuristic to minimize the Bethe free energy. Focusing on binary pairwise models, we demonstrate that several recent results on the Bethe approximation may be generalized to a broad family of related pairwise free energy approximations with arbitrary counting numbers. We explore approximation error and shed light on the empirical success of the Bethe approximation. |
Year | Venue | Field |
|---|---|---|
2015 | UAI | Pairwise comparison,Mathematical optimization,Heuristic,Inference,Computer science,Graphical model,Approximation error,Binary number,Belief propagation |
DocType | Citations | PageRank |
Conference | 3 | 0.40 |
References | Authors | |
17 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrian Weller | 1 | 141 | 27.59 |