Lars Ruthotto

MATH 789R - Bayesian Inverse Problems and Uncertainty Quantification

Instructor:
Prof. Lars Ruthotto
Syllabus:
The syllabus can be downloaded here.
Material:
Homework and additional course material will be made available using Blackboard.
Times:
MoWe 3:00 - 4:15 PM, Math & Science Center, Room E406
First day of classes: January 11, 2016
Last day of classes: April 25, 2016
Recess: January 18 (Martin Luther King Day), March 7 and 9 (Spring Break)
Description:
This special topics course introduces basic concepts as well as more recent advances in Bayesian methods for solving inverse problems. Motivated by real-world applications, we will contrast the frequentists and the Bayesian approach to inverse problems and emphasize the role of regularization/priors. Also, we will explore sampling techniques used for uncertainty quantification.
The course introduces relevant theory from discrete probability.
Literature:
The main references for this course are:
An Introduction to Bayesian Scientific Computing by E. Somersalo and D. Calvetti
Statistical and Computational Inverse Problems by J. Kaipio and E. Somersalo
Additional material will be assigned as needed.
Homework:
Homework will be a combination of computing and analysis. Computing can be done using Julia or Matlab.
Solutions, results, and analysis should be submitted as a single, readable document. This document can either be sent to me electronically (as a pdf file), or you can give me a printed copy.
All codes used to generate results for the assignments have to be submitted electronically as a single .zip, .tar, or .tgz archive.
Attendance:
Attendance is not required, but strongly encouraged.
If you miss a class, then you should get a copy of the notes from one of your classmates.
Conduct:
All students must adhere to the provisions of the Graduate Student Conduct Code. For more information, see page 29 of the graduate student handbook.