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Tlis paper addresses several aspects of the analysis of uncertainty in the output of computer models arising from uncertainty in inputs (parameters). Uncertainty of this type, which is separate and distinct from the randomness of a stochastic model, most often arises when input values are guesstimates, or when they are estimated from data, or when the input parameters do not actually correspond to observable quantities, e.g., in lumped-parameter models. Uncertainty in the output is quantified in its probability distribution, which results from treating the inputs as random variables. The assessment of which inputs are important with respect to uncertainty is done relative to the probability distribution of the output.

Latin hypercube sampling as a tool in uncertainty analysis of computer models