Approximate Bayesian Inference : Relaxations, Algorithms and Large Scale Applications
Event details
| Date | 28.06.2010 |
| Hour | 16:00 |
| Speaker | Matthias Seeger, Saarland University and Max Planck Institute for Informatics |
| Location |
INM200
|
| Category | Conferences - Seminars |
Abstract:
Today's real world problems of large scale information processing demand
decision making from uncertain knowledge, not only in high level domains like
language understanding or intelligent behaviour, but increasingly so in
fundamental fields of signal and image processing, if only to circumvent
spiralling hardware costs or statistical limits coming with classical
approaches. Modern science
and medicine, with questions growing more rapidly than data streams, need
autonomous tools for acquisition and sifting of data or experimental planning.
Bayesian graphical modelling is the preeminent language and calculus for
working with uncertain information, but poses hard computational challenges in
practice. Some of these have successfully been addressed in machine learning,
calling on ideas from convex optimization, numerical mathematics, and graph theory.
More information on : http://people.mmci.uni-saarland.de/~mseeger/
Practical information
- General public
- Free
Contact
- Christine Moscioni