## A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests (2006)

### Cached

### Download Links

- [www.jmlr.org]
- [decsai.ugr.es]
- [decsai.ugr.es]
- [jmlr.csail.mit.edu]
- [jmlr.org]
- DBLP

### Other Repositories/Bibliography

Venue: | JOURNAL OF MACHINE LEARNING RESEARCH |

Citations: | 17 - 0 self |

### BibTeX

@MISC{Campos06ascoring,

author = {Luis M. de Campos},

title = { A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests},

year = {2006}

}

### OpenURL

### Abstract

We propose a new scoring function for learning Bayesian networks from data using score search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.