A Bayesian posterior is used to center KL-divergence ambiguity sets for distributionally robust optimization, and for conjugate exponential families the worst-case problem reduces to a single-stage stochastic program.
Real-world datasets for portfolio selection and solutions of some stochastic dominance portfolio models
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Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
A Bayesian posterior is used to center KL-divergence ambiguity sets for distributionally robust optimization, and for conjugate exponential families the worst-case problem reduces to a single-stage stochastic program.