| BNSL-package {BNSL} | R Documentation |
Bayesian Network Structure Learning
Description
From a given dataframe,this package learn a Bayesian network structure based on a seletcted score.
Details
Currently,this package estimates of mutual information and conditional mutual information, and combines them to construct either a Bayesian network or a undirected forest, any undirected forest can be a Bayesian network by adding appropriate directions.
Author(s)
Joe Suzuki and Jun Kawahara
Maintainer: Joe Suzuki <j-suzuki@sigmath.es.osaka-u.ac.jp>
References
[1] Suzuki, J., “A theoretical analysis of the BDeu scores in Bayesian network structure learning", Behaviormetrika, 2017. [2] Suzuki, J., “A novel Chow-Liu algorithm and its application to gene differential analysis", International Journal of Approximate Reasoning, 2017. [3] Suzuki, J., “Efficient Bayesian network structure learning for maximizing the posterior probability", Next-Generation Computing, 2017. [4] Suzuki, J., “An estimator of mutual information and its application to independence testing", Entropy, Vol.18, No.4, 2016. [5] Suzuki, J., “Consistency of learning Bayesian network structures with continuous variables: An information theoretic approach". Entropy, Vol.17, No.8, 5752-5770, 2015. [6] Suzuki. J., “Learning Bayesian network structures when discrete and continuous variables are present. In Lecture Note on Artificial Intelligence, the sixth European workshop on Probabilistic Graphical Models, Vol. 8754, pp. 471-486,Utrecht, Netherlands, Sept. 2014. Springer-Verlag. [7] Suzuki. J., “The Bayesian Chow-Liu algorithms", In the sixth European workshop on Probabilistic Graphical Models, pp. 315-322, Granada, Spain, Sept.2012. [8] Suzuki, J. and Kawahara, J., “Branch and Bound for Regular Bayesian Network Structure learning", Uncertainty in Artificial Intelligence, pages 212-221, Sydney, Australia, August 2017. [9] Suzuki, J. “Forest Learning from Data and its Universal Coding", IEEE Transactions on Information Theory, Dec. 2018. January 2017.