Uncertainty Quantification

1 Unit · Graduate

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.