University of Cambridge > Talks.cam > CCIMI Seminars > Reconciling Bayesian Regularization And Total Variation Regularization - CCIMI colloquium

Reconciling Bayesian Regularization And Total Variation Regularization - CCIMI colloquium

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Rachel Furner.

Free event, but registration required: https://ccimi-colloquium-andrew-stuart.eventbrite.co.uk

The first CCIMI Colloquium will feature a talk by Andrew Stuart on ‘Reconciling Bayesian Regularization And Total Variation Regularization’.  This will be held on Wednesday 8th May at 5pm, at the Centre for Mathematical Sciences, University of Cambridge, and followed with a wine reception.

Abstract: Joint work with Matt Dunlop (Caltech), Charlie Elliott (Warwick), and Viet Ha Haong (NTU). A central theme in classical algorithms for the reconstruction of discontinuous functions from observational data is perimeter regularization, for example via total variation penalization. On the other hand, sparse or noisy data often demands a probabilistic approach to the reconstruction of images, to enable uncertainty quantification; the Bayesian approach to inversion is a natural framework in which to carry this out. The link between Bayesian inversion methods and perimeter regularization, however, is not fully understood. In this talk two links are studied: (i) the MAP objective function of a suitably chosen phase-field Bayesian approach is shown to be closely related to a least squares plus perimeter regularization objective; (ii) sample paths of a suitably chosen Bayesian level set formulation are shown to possess finite perimeter and to have the ability to learn about the true perimeter. Furthermore, the level set approach is shown to lead to faster algorithms for uncertainty quantification than the phase field approach.

Speaker: Andrew Stuart Andrew Stuart has research interests in applied and computational mathematics, and is interested in particular in the question of how to optimally combine complex mechanistic models with data. He joined Caltech in 2016 as Bren Professor of Computing and Mathematical Sciences, after 17 years as Professor of Mathematics at the University of Warwick (1999—2016). Prior to that he was on the faculty in The Departments of Computer Science and Mechanical Engineering at Stanford University (1992—1999), and in the Mathematics Department at Bath University (1989—1992). He obtained his PhD from the Oxford University Computing Laboratory in 1986, and held postdoctoral positions in Mathematics at Oxford University and at MIT in the period 1986—1989

The event is free, but please register: https://ccimi-colloquium-andrew-stuart.eventbrite.co.uk

This talk is part of the CCIMI Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity