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Once the model has been sufficiently disaggregated, it is usually not necessary to provide very precise estimates of each individual component of the model. In fact, three point estimates are often sufficient: the minimum, most likely and maximum values the expert believes the value could take. These three values can be used to define either a Triangular distribution or some form of PERT distribution. Our preference is to use a PERT, as described here, because it has a natural shape that will invariably match the experts view better than a Triangular distribution would. The analyst should attempt to determine the expert's opinion of the maximum value first and then the minimum, by considering scenarios that could produce such extremes. Then, the expert should be asked for his/her opinion of the most likely value within that range. Determining the parameters in the order: 1. maximum, 2. minimum and 3.most likely will go some way to removing the anchoring bias. 

Occasionally, a model will not disaggregate evenly into sufficiently small components, leaving the model's outputs strongly affected by one or more individual subjective estimates. When this is the case, it is useful to employ a more rigorous approach to eliciting an expert's opinion than a simple three point estimate. In such cases, the PERT distribution is a good starter but, on review of the plotted distribution, the expert might still want to modify the shape a little. In this case, the analyst can always construct a General distribution in Crystal Ball that exactly fits the expert's opinion.