Name
Abstract | ||
|---|---|---|
While a great variety of algorithms have been developed and applied to learning static Bayesian networks, the learning of dynamic networks has been relatively neglected. The causal discovery program CaMML has been enhanced with a highly flexible set of methods for taking advantage of prior expert knowledge in the learning process. Here we describe how these representations of prior knowledge can be used instead to turn CaMML into a promising tool for learning dynamic Bayesian networks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-35101-3_76 | Australasian Conference on Artificial Intelligence |
Keywords | Field | DocType |
dynamic network,flexible set,static bayesian network,dynamic bayesian network,prior expert knowledge,causal discovery program,promising tool,great variety,prior knowledge | Intelligent control,Variable-order Bayesian network,Computer science,Bayesian network,Artificial intelligence,Machine learning,Dynamic Bayesian network | Conference |
Citations | PageRank | References |
2 | 0.36 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cora Beatriz Pérez-Ariza | 1 | 3 | 0.74 |
| Ann E. Nicholson | 2 | 692 | 88.01 |
| Kevin B. Korb | 3 | 400 | 52.03 |
| Steven Mascaro | 4 | 45 | 4.11 |
| Chao Heng Hu | 5 | 2 | 0.36 |