Northwestern University 1 credit Industrial Engineering & Management Sciences
Computational techniques for modern uncertainty quantification which includes uncertainty representation, uncertainty propagation, surrogate inference, calibration, approximate inference and evaluation. Covers computational tools including Monte Carlo, sensitivity analysis, quasi-Monte Carlo, Markov chain Monte Carlo, variational inference, experimental design, divergences, and scoring rules. Prerequisites: Graduate students with an understanding of graduate level statistics and probability and being comfortable with a scripting language like python, R, or matlab will prove helpful.
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