Name
Abstract | ||
|---|---|---|
Compiling graphical models has recently been triggered much research. First investigations were established in the probabilistic framework. This paper studies compilation-based inference in min-based possibilistic networks. We first take advantage of the idempotency property of the min operator to enhance an existing compilation-based inference method in the possibilistic framework. Then, we propose a new CNF encoding which fits well with the particular case of binary networks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33042-1_51 | SYNERGIES OF SOFT COMPUTING AND STATISTICS FOR INTELLIGENT DATA ANALYSIS |
Keywords | Field | DocType |
Compilation,inference,possibilistic reasoning | Discrete mathematics,Computer science,Inference,Local structure,Operator (computer programming),Graphical model,Idempotence,Encoding (memory),Binary number,Probabilistic framework | Conference |
Volume | ISSN | Citations |
190 | 2194-5357 | 1 |
PageRank | References | Authors |
0.37 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raouia Ayachi | 1 | 52 | 8.02 |
| Nahla Ben Amor | 2 | 467 | 50.72 |
| Salem Benferhat | 3 | 2585 | 216.23 |