MIT Subject Listing & Schedule
Fall 2018 Search Results

Searched for: "16.940"    Subjects offered any term

1 subject found.

16.940 Numerical Methods for Stochastic Modeling and Inference

()
Prereq: (6.431B and 16.920) or permission of instructor
Units: 3-0-9
Lecture: TR1-2.30 (35-225)

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.
Y. M. Marzouk
Textbooks (Fall 2018)