16.940 Numerical Methods for Stochastic Modeling and Inference
Fall 2015
Instructor: Youssef M Marzouk
Additional instructors: Florian Markus Augustin, Daniele Bigoni, Matthew David Parno
Lecture: TR 10:30–12:00 (33-319)
Information:
Advanced introduction to numerical methods for treating
uncertainty in computational simulation. Draws examples from a
range of science and engineering 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-Loève 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,
model selection, and model error. Computer assignments require
programming.
Announcements
Project #1 posted
This project will let you practice with Monte Carlo methods, importance sampling, and rare event simulation. Due on 13 Oct 2015.Announced on 28 September 2015 1:38 a.m. by Youssef M Marzouk
