This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. posteriors afford principled mechanism to incorporate data and prior knowledge into problems. However, the computation of is typically an intractable problem, has spawned large literature on approximate computation. Here, context optimization, we focus question statistical consistency (...

In this paper we consider finite-horizon predictive control of dynamic systems subject to stochastic uncertainty; such uncertainty arises due to exogenous disturbances, modeling errors, and sensor noise. Stochastic robustness is typically defined using chance constraints, which require that the probability of state constraints being violated is below a prescribed value. Prior work showed that i...