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SUMMARY:Causal network structure identification in nonlinear dynamical sys
 tems - Professor Zoubin Ghahramani (Department of Engineering)
DTSTART:20081023T085500Z
DTEND:20081023T092000Z
UID:TALK13228@talks.cam.ac.uk
CONTACT:Duncan Simpson
DESCRIPTION:One of the central challenges of understanding complex systems
 \, such as financial markets\, neural circuits\, and cellular information 
 processing networks\,  is to identify which system components are causally
  related. This work introduces a probabilistic framework for learning the 
 causal structure of sparsely coupled nonlinear dynamical systems from obse
 rved time series data. The proposed algorithm adopts a continuous time Gau
 ssian Process model of the system dynamics and provides an estimated distr
 ibution over directed network topologies representing the latent interacti
 on among system components. The method is shown to identify robustly the t
 opological structure of a diverse class of synthetic gene regulatory netwo
 rks (joint work with Sandy Klemm and Karsten Borgwardt). 
LOCATION:Kaetsu Centre\, New Hall
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