Name
Abstract | ||
|---|---|---|
We suggest Darwinian Networks (DNs) as a simplification of working with Bayesian networks (BNs). DNs adapt a handful of well-known concepts in biology into a single framework that is surprisingly simple yet remarkably robust. With respect to modeling, on one hand, DNs not only represent BNs but also faithfully represent the testing of independencies in a more straightforward fashion. On the other hand, with respect to three exact inference algorithms in BNs, DNs simplify each of them while unifying all of them. DNs can determine good elimination orderings using the same platform as used for modeling and inference. Finally, we demonstrate how DNs can represent two additional frameworks. Practical benefits of DNs include faster algorithms for inference and modeling. |
Year | Venue | Keywords |
|---|---|---|
2017 | COMPUTATIONAL INTELLIGENCE | bayesian networks,d-separation,variable elimination,join tree propagation |
DocType | Volume | Issue |
Journal | 33.0 | 4.0 |
ISSN | Citations | PageRank |
0824-7935 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cory J. Butz | 1 | 383 | 40.80 |
| Jhonatan de S. Oliveira | 2 | 6 | 7.43 |
| André E. dos Santos | 3 | 5 | 7.02 |