You can use our structure learning module to build the structure of the graph or to discover dependencies through available data.
Features
Nodes dependency discovery.
Hypothesis testing.
Model and variable selection.
References
- Koller D, Friedman N. Probabilistic graphical models: principles and techniques. MIT press; 2009.
- Sucar LE. Probabilistic graphical models. Advances in Computer Vision and Pattern Recognition. London: Springer London. doi. 2015;10(978):1.
- 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.
- 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.
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