MODEL CALIBRATION
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.
