Name
Abstract | ||
|---|---|---|
•Semiparametric Bayesian networks mix parametric and non-parametric estimation models.•This proposal generalize other well-known continuous Bayesian networks.•Two learning algorithms based on greedy hill-climbing and PC are proposed.•The combination of parametric and non-parametric estimation models can be learned. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2021.10.074 | Information Sciences |
Keywords | DocType | Volume |
Bayesian networks,Kernel density estimation,Semiparametric model,Continuous data | Journal | 584 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.63 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David Atienza | 1 | 1 | 0.63 |
| Concha Bielza | 2 | 909 | 72.11 |
| Pedro Larrañaga | 3 | 3882 | 208.54 |