After creating a graph, conditional probability distribution parameters can be calibrated using historical data or reflecting expert judgement.
Features
Several optimization techniques available.
Start simple with regression under linear assumptions on node dependencies.
Incorporate expert judgment in parameterizing nodes distributions.
Data imputation techniques available for incomplete datasets.
Frequency matching.
Preprocessing methods available prior to calibration.
Data transformation routines. Node types: Categorical, Discrete, Continuous, Mixture, Deterministic.
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
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