Go to start page
 Search IMMs web site
 Help and FAQ
 All Faculty PhD Students Staff Guests
Sections
 Computer Engineering and Technology
 Computer Science and Technology
 Geoinformatics
 Image Analysis & Computer Graphics
 Mathematical Physics
 Numerical Analysis
 Operations Research
 Signal Processing
 Statistics
Publications
Projects
Conferences
Professional activities
 Info for students Continuing Education B.Sc. Courses M.Sc. Courses Ph.D. Courses Graduate Schools M.Sc. Projects B.Sc. Projects Midterm Projects
 Search IMM Web News and Events Job vacancies Contact Student info Library
 Contact information
 Page maintained by Finn Kuno Christensen (fkc@imm.dtu.dk)
 IMM restricted extranet
 Informatics and Mathematical Modelling Technical University of Denmark
 People Research Teaching Profile Info

# [Using Bayesian Techniques to Solve Inverse Problems]

Project title: Using Bayesian Techniques to Solve Inverse Problems

Persons to contact: Prof. Per Christian Hansen, building 305, room 105, phone +45 4525 3097 - pch@imm.dtu.dk or Prof. Ole Winther, building 321, room 115, phone +45 4525 3895 - owi@imm.dtu.dk

Background: Inverse problems appear in modelling of for example tomography and geophysical problems. The inverse problem can be recast into a statistical one by expressing all information, i.e., observed data and regularization, in terms of probability distributions. Using Bayes theorem, the information can be combined to make inference about the problem. Inference means both finding a solution, assigning errors bars to it and estimating other quantities like measurement noise.

Project formulation: A number of projects are possible. For example, it can be studied how the linear algebra formulation of inverse problem can be translated to Bayesian theory. By combining these, algorithms that exploit the best of both views should be developed.

In other cases variational techniques (i.e., approximate the complicated distribution with a simpler tractable one) and Monte Carlo techniques (i.e., sample the distribution) can be used. It can be studied how these scale and perform for large artificial and real-world problems.

Forudsætninger: 02401 Dataanalyse og indledende statistik eller tilsvarende, samt et videregående fag i numerisk analyse/scientific computing.

Antal deltagere: 1-3.

Consult DTU campus catalogue: IMM Master in Engineering courses.