Stochastic Programming: Approximation via Scenarios

We shall survey and classify various ways of approximation of the true probability distribution by a discrete distribution concentrated in a nite number of atoms, called scenarios, which occur with preas-signed probabilities. Resistance of the numerical results with respect to the choice of scenarios and/or of their probabilities and also the relation between results obtained for the selected scenarios and those valid for the true underlying distribution are of a great practical interest. We shall discuss these questions bearing in mind the diverse origin of scenarios that puts limitations both on the choice of suitable approaches to the analysis and on the obtained conclusions.

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