AUTUMN 2020: CAAM 31150=STAT 31150

Course: CAAM 31150=STAT 31150

Title: Inverse Problems and Data Assimilation

Instructor(s): Daniel Sanz-Alonso

Teaching Assistant(s):

Class Schedule: Sec 01: TuTh 2:40 PM–4:00 PM

Office Hours: 

Textbook(s):

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.