16.940 Numerical Methods for Stochastic Modeling and Inference
Advanced introduction to numerical methods for treating uncertainty in computational simulation. Draws examples from a range of engineering and science applications, emphasizing systems governed by ordinary and partial differential equations. Uncertainty propagation and assessment: Monte Carlo methods, variance reduction, sensitivity analysis, adjoint methods, polynomial chaos and Karhunen-Loeve expansions, and stochastic Galerkin and collocation methods. Interaction of models with observational data, from the perspective of statistical inference: Bayesian parameter estimation, statistical regularization, Markov chain Monte Carlo, sequential data assimilation and filtering, and model selection.
16.940 will be offered this semester (Fall 2018). It is instructed by Y. M. Marzouk.
Lecture occurs 1:00 PM to 2:30 PM on Tuesdays and Thursdays in 56-162.
This class counts for a total of 12 credits.
You can find more information at the MIT + 16.940 - Google Search site.
© Copyright 2015 Yasyf Mohamedali