BIRS Workshop Lecture Videos

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BIRS Workshop Lecture Videos

Model Uncertainty and Robust Stochastic Modeling Lam, Henry


Virtually any performance analysis in stochastic modeling relies on input model assumptions that, to some extent, deviate from the truth. This talk will investigate a worst-case framework to quantify these model errors and correspondingly robustify the stochastic outputs. It entails posting optimization programs over the input probability distributions, with constraints representing the modeler’s partial, nonparametric knowledge about them. We illustrate these optimization formulations in several contexts in stochastic modeling, describe their computational challenges, and present some machinery in approximating their solutions.

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