Estimation in chance-constrained problem

Many engineering and economic applications make use of the stochastic programming theory. Major part of models require a complete knowledge of distribution of random parameters, but this assumption is rarely accomplished. We then need to study behaviour of optimal solutions when the distribution changes slightly. In our contribution we consider the chance constrained problem; we recapitulate some known theoretical results about stability and estimation of the problem. We concentrate especially on results from stochastic and robust programming; we try to outline a link between empirical and robust estimates of chance-constrained problem.