Develops and analyzes approximate methods of solving the Bayesian inverse problem for high-dimensional dynamical systems. After briefly reviewing mathematical foundations in probability and statistics, the course covers the Kalman filter, particle filters, variational methods and ensemble Kalman filters. The emphasis is on mathematical formulation and analysis of methods. Same as APPM 4510, STAT 4250 and STAT 5250.
instructor(s)
Grooms, Ian G
Primary Instructor
- Fall 2019 / Fall 2021 / Fall 2023
