journal article
LitStream Collection
Deterministic approximations of probability inequalities
doi: 10.1007/BF01423332pmid: N/A
A simple general framework for derivingexplicit deterministic approximations of probability inequalities of the formP(ξ⩾a) ⩽ α is presented. These approximations are based on limited parametric information about the involved random variables (such as their mean, variance, range or upper bound values). First the case of a single random variableξ is analysed, followed by the cases of independent and dependent summands $$\xi = \mathop \sum \limits_1^n \xi _i $$ . As examples of possible applications, a stochastic extension of the “knapsack problem” and the stochastic linear programming problem with separate chance-constraints are investigated: we provide approximate deterministic surrogates for these problems.