Lars Ruthotto

MATH 789R - Bayesian Inverse Problems and Uncertainty Quantification

Prof. Lars Ruthotto
The syllabus can be downloaded here.
Homework and additional course material will be made available using Blackboard.
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)
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.
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 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 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.
All students must adhere to the provisions of the Graduate Student Conduct Code. For more information, see page 29 of the graduate student handbook.