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.

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 : OR uniformPDF : symmetric=['yes'|'no'] bounds [, bounds]

`uncertainty` attributes:

`effect`Indicates, by choice of four enumerated values, how the uncertainty is modeled: as an additive, multiplicative, or percentage variation about the nominal value, or an specific number (absolute).

`uncertainty`
sub-elements:

`normalPDF`If present, the uncertainty in the parameter value has a probability distribution that is Gaussian (bell-shaped). A single parameter representing the additive (+/- some value), percentage (+/- some %) of variation from the nominal value in terms of 1, 2, 3, or more standard deviations (sigmas) is specified. Note here multiplicative and absolute bounds don't make much sense.

`uniformPDF`If present, the uncertainty in the parameter or function value has a uniform likelihood of taking on any value between symmetric or asymmetric boundaries, which are specified in terms of additive (either +/-x or +x/-y), multiplicative, percentage, or absolute variations. The specified range of values must bracket the nominal value. For this element, the

`bounds`sub-element may contain one or two values in which case the boundaries are symmetric or asymmetric.