A simple general framework for deriving explicit deterministic approximations of probability inequalities 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). As examples of possible applications, a stochastic extension of the ‘knapsack problem’ and the stochastic linear programming problem with separate chance-constraints are investigated: the paper provides approximate deterministic surrogates for these problems.
