EE375 Lecture 15d: Numerical Uncertainty Propagation
Channel: Michael Dietze
Date: 2020-10-19

Covers the numerical Monte Carlo method for propagating uncertainty into a prediction my running a model repeatedly under different samples of inputs. Touches on how this allows us to address multiple uncertainties at once and get around Jensen's Inequality in nonlinear models. Makes a distinction between a full Monte Carlo approach, where we run enough ensemble members to approximate a distribution, and ensemble approaches, where the number of runs is smaller and we have to make distributional assumptions and approximate predictions based on their mean and variance. Provides R code showing how to implement these methods.