algorithms5 {bnRep} | R Documentation |
algorithms Bayesian Networks
Description
Entropy and the Kullback-Leibler divergence for Bayesian networks: Computational complexity and efficient implementation.
Format
A conditional linear Gaussian Bayesian network to illustrate the algorithms developed in the associated paper (Figure 3, top). The probabilities were available from a repository. The vertices are:
- X1
(a, b);
- X2
(c, d);
- X3
(e, f);
- X4
- X5
- X6
Value
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
References
Scutari, M. (2024). Entropy and the Kullback-Leibler Divergence for Bayesian Networks: Computational Complexity and Efficient Implementation. Algorithms, 17(1), 24.
[Package bnRep version 0.0.1 Index]