Winter 2019 STAT 31550

Course: STAT 31550
Title: Uncertainty Quantification
Instructor(s): Daniel Sanz-Alonso
Class Schedule: Sec 01: TR 11:00 AM–12:20 PM in Jones 226
Office Hours:
Textbook(s): None 
Description: This course will cover mathematical, statistical, and algorithmic questions that arise at the interface of complex modeling and data processing. Emphasis will be given to characterizing and quantifying the uncertainties inherent in the use of models and to exploring principled ways to reduce said uncertainty by the use of data. Specific topics include Bayesian inverse problems and data assimilation.

Prerequisite(s): STAT 30200 or consent of instructor