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