MAE 279Uncertainty Quantification
This course is an introduction to uncertainty quantification and will cover basic and advanced computational methods to quantify uncertainties in parameters, systems, and simulation. Covers basics in statistical and stochastic modeling, Monte Carlo sampling techniques including variance reduction and importance sampling, sensitivity analysis, Bayes’ theorem and related Bayesian inference methods (variants of Markov chain Monte Carlo).
Prerequisites
From the catalog: “graduate standing.”