Stability and Approximation of Projection Filters
When and Where
Speakers
Description
Nonlinear filtering is a central mathematical tool in understanding how we process information. Sadly, the equations involved are often very high dimensional, which may lead to difficulties in applications. One possible resolution (due to D. Brigo and collaborators) is to replace the filter by a low-dimensional approximation, with hopefully small error. In this talk we will see how, in the case where the underlying process is a finite-state Markov Chain, results on the stability of filters can be strengthened to show that this introduces a well-controlled error, leveraging tools from information geometry.
(Based on joint work with Eliana Fausti)
About Samuel Cohen
Dr. Samuel Cohen received his doctorate from the University of Adelaide in 2011 under the supervision of Robert Elliott and Charles Pearce. He is a Professor in the Mathematical Institute at the University of Oxford and the theme lead for Machine Learning in Finance at the Alan Turing Institute. He is also an associate member of the Oxford-Man Institute, a member of the Oxford-Nie Financial Big Data Lab, and a Senior Research Fellow at New College. He is the current chair of SIAM Financial Mathematics and his research interests are in the interactions between statistical and machine learning, decision making, and control and uncertainty aversion.