algorithms4 dataset

algorithms Bayesian Networks

Entropy and the Kullback-Leibler divergence for Bayesian networks: Computational complexity and efficient implementation. data

Format

A discrete Bayesian network to illustrate the algorithms developed in the associated paper (Figure 2, bottom). The probabilities were available from a repository. The vertices are:

• X1: (a, b);
• X2: (c, d);
• X3: (e, f);
• X4: (g, h);

Returns

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.