You can use our structure learning module to build the structure of the graph or to discover dependencies through available data.


  • Nodes dependency discovery.

  • Hypothesis testing.

  • Model and variable selection. 


[1]  Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009. - III Learning - 18. Structure Learning in Bayesian Networks p783

[2]  Sucar LE. Probabilistic graphical models. Advances in Computer Vision and Pattern Recognition. London: Springer London. doi. 2015;10(978):1.

[3]  Darwiche A. Modeling and reasoning with Bayesian networks. Cambridge university press; 2009 Apr 6. - 17 Learning: The Maximum Likelihood Approach p439, 18 Learning: The Bayesian Approach p477

[4]  Koller D, Friedman N, Džeroski S, Sutton C, McCallum A, Pfeffer A, Abbeel P, Wong MF, Meek C, Neville J, Jensen D. Introduction to statistical relational learning. MIT press; 2007.