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-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, and model selection.
16.940 will not be offered this semester. It will be available in the Fall semester, and will be instructed by Y. M. Marzouk.
This class counts for a total of 12 credits. This is a graduate-level class.
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