Statistical measures of variation of certain parameters and functions can be embedded in a DAVE-ML model. This information is captured in a uncertainty element, which can be referenced by variableDef, griddedTableDef and ungriddedTableDef elements.

Uncertainty in the value of a parameter or function is given in one of two ways, depending on the appropriate probability distribution function (PDF): as a Gaussian or normal distribution (bell curve) or as a uniform (evenly spread) distribution. One of these distributions is selected by including either a normalPDF or a uniformPDF element within the uncertainty element.

Linear correlation between the randomness of two or more variables or functions can be specified. Although the correlation between parameters do not have a dependency direction (that is, the statistical uncertainty of either parameter is specified in terms of the other one so the calculation order doesn't matter) correlation is customarily specified as a dependency of one random variable on the value of another random variable. correlatesWith identifies variables or functions whose uncertainty 'depends' on the current value of this variable or parameter; the correlation subelement specifies the correlation coefficient and identifies the (previously calculated) random variable or function on which the correlation depends.

These correlation subelements only apply to normal (Gaussian) probability distribution functions.

Each of these distribution description elements contain additional information, as described below.

    uncertainty : effect=['additive'|'multiplicative'|'percentage'|'absolute']
      EITHER
        normalPDF : numSigmas=['1', '2', '3', ...]
            bounds :
           [correlatesWith : varID]
           [correlation : varID, corrCoef]
      OR
        uniformPDF : symmetric=['yes'|'no']
            bounds [, bounds]