Autumn 2020 STAT 31150

Course: STAT 31150=CAAM 31150

Title: Inverse Problems and Data Assimilation

Instructor(s):  Daniel Sanz-Alonso

Teaching Assistant(s): TBA

Class Schedule: Sec 01: TR 2:40 PM–4:00 PM in TBA

Description: This class provides an introduction to Bayesian Inverse Problems and Data Assimilation, emphasizing the theoretical and algorithmic inter-relations between both subjects. We will study Gaussian approximations and optimization and sampling algorithms, including a variety of Kalman-based and particle filters as well as Markov chain Monte Carlo schemes designed for high-dimensional inverse problems.

Prerequisite(s): Familiarity with calculus, linear algebra, and probability/statistics at the level of STAT 24410. Some knowledge of STAT 24400 or STAT 24410. Some knowledge of ODEs may also be helpful.