Editing Stochastic programming (section)

=== Distributional assumption ===
The formulation of the above two-stage problem assumes that the second-stage data <math>\xi</math> is modeled as a random vector with a '''''known''''' probability distribution. This would be justified in many situations. For example, the distribution of <math>\xi</math> could be inferred from historical data if one assumes that the distribution does not significantly change over the considered period of time. Also, the empirical distribution of the sample could be used as an approximation to the distribution of the future values of <math>\xi</math>. If one has a prior model for <math>\xi</math>, one could obtain a posteriori distribution by a Bayesian update.