Name
Abstract | ||
|---|---|---|
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or other more general exponential models. This result generalizes the well known Hammersley-Clifford Theorem. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence | sufficient condition,general exponential model,log-linear model,undirected graphical model,hammersley-clifford theorem,arbitrary discrete probability distribution,probability distribution |
DocType | Volume | ISBN |
Journal | abs/1301.0568 | 1-55860-897-4 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan Geiger | 1 | 3371 | 570.49 |
| Christopher Meek | 2 | 554 | 70.15 |
| Bernd Sturmfels | 3 | 926 | 136.85 |