UCL ELLIS

Speaker	Amos Storkey
Affiliation	Edinburgh University
Date	Friday, 21 November 2014
Time	13:00-14:00
Location	Zoom
Link	Roberts G08 (Sir David Davies lecture theatre)
Event series	Jump Trading/ELLIS CSML Seminar Series
Abstract	Many systems in science, engineering and finance are described using known forms of parameterised differential equations. Often there are unknown random influences to such systems and so a stochastic differential system is an appropriate model. However inference and learning in general nonlinear stochastic differential systems is notoriously hard, primarily because the finite time transition probability cannot be explicitly represented. I will discuss the series expansion approach for approximating a diffusion, and demonstrate examples of the method in direct application for parameter estimation in diffusion processes, and via nonlinear Kalman filters and the unscented particle filter. This talk describes joint work with Simon Lyons and Simo Sarkka, and was funded by a MSR Cambridge PhD fellowship. Bio: Amos Storkey is a reader (associate professor) at the School of Informatics, Edinburgh University. He did his PhD in Neural Networks at the Neural Systems Group, Imperial College, London. His research interests include: machine learning markets; Bayesian methods for brain imaging; continuous time/depth systems; dynamical Boltzmann machine models; scalable deep learning. Video of the talk here.
Biography