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SUMMARY:Causal Network Structure Identification in Nonlinear Dynamical Sys
 tems - Professor Zoubin Ghahramani\, Machine Learning\, University of Camb
 ridge
DTSTART:20090209T160000Z
DTEND:20090209T170000Z
UID:TALK16650@talks.cam.ac.uk
CONTACT:Dr Clive Bowsher
DESCRIPTION:One of the central challenges of understanding complex systems
 --such as financial markets\, neural circuits\, and cellular information p
 rocessing networks--is to identify which system components are causally re
 lated. This work introduces a probabilistic framework for learning the cau
 sal structure of sparsely coupled nonlinear dynamical systems from observe
 d time series data. The proposed algorithm adopts a continuous time Gaussi
 an Process model of the system dynamics and provides an estimated distribu
 tion over directed network topologies representing the latent interaction 
 among system components. The method is shown to identify robustly the topo
 logical structure of a diverse class of synthetic gene regulatory networks
 . (Joint work with Sandy Klemm and Karsten Borgwardt.)\n
LOCATION:Centre for Mathematical Sciences\, MR15
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