Data-driven approximation of distributionally robust chance constraints using Bayesian credible intervals

The non-convexity and intractability of distributionally robust chance constraints make them challenging to cope with. From a data-driven perspective, we propose formulating it as a robust optimization problem which can ensure that the distributionally robust chance constraint is satisfied with high probability. To incorporate available data and prior distribution knowledge, we construct ambiguity sets for distributionally robust chance constraints using Bayesian credible intervals. We establish the congruent relationship between the ambiguity set in the Bayesian distributionally robust chance constraint and the uncertainty set in a specific robust optimization. In contrast to most existent uncertainty set construction methods which are only applicable for particular settings, our approach provides a unified and flexible framework for constructing uncertainty sets under different marginal distribution assumptions. Additionally, under the concavity assumption, our method provides strong finite sample probability guarantees for feasible solutions. The practicality and effectiveness of our approach are illustrated with numerical experiments on portfolio management and queuing system problems. Overall, our approach offers a promising solution to distributionally robust chance constrained problems and has potential applications in other fields.