Course: CAAM 31150=STAT 31150
Title: Inverse Problems and Data Assimilation
Instructor(s): Daniel Sanz-Alonso
Teaching Assistant(s): Yuming Chen
Class Schedule: Sec 01: TuTh 11:00 AM–12:20 PM in Jones Laboratory 226
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.
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.