3 papers
cs.AI2018
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
Mauro Scanagatta, Giorgio Corani, Marco Zaffalon +2
Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, w…
cs.AI2017
Entropy-based Pruning for Learning Bayesian Networks using BIC
Cassio P. de Campos, Mauro Scanagatta, Giorgio Corani +1
For decomposable score-based structure learning of Bayesian networks, existing approaches first compute a collection of candidate parent sets for each variable and then optimize ov…
cs.AI2016
Learning Bounded Treewidth Bayesian Networks with Thousands of Variables
Mauro Scanagatta, Giorgio Corani, Cassio P. de Campos +1
We present a method for learning treewidth-bounded Bayesian networks from data sets containing thousands of variables. Bounding the treewidth of a Bayesian greatly reduces the comp…