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.

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