BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Generalised Particle Filters with Gaussian Mixtures - Li\, K (Upps
 ala University)
DTSTART:20140425T105000Z
DTEND:20140425T112500Z
UID:TALK52186@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Stochastic filtering is defined as the estimation of a partial
 ly observed dynamical system.\nA massive scientific and computational effo
 rt has been dedicated to the development of numerical methods for approxim
 ating the solution of the filtering problem. Approximating with Gaussian m
 ixtures has been very popular since the 1970s\, however the existing work 
 is only based on the success of the numerical implementation and is not th
 eoretically justified.\n\nWe fill this gap and conduct a rigorous analysis
  of a new Gaussian mixture approximation\nto the solution of the filtering
  problem. In particular\, we construct the corresponding approximating alg
 orithm\, deduce the L2-convergence rate and prove a central limit type the
 orem for the approximating system. In addition\, we show a numerical examp
 le to illustrate some features of this algorithm. This is joint work with 
 Dan Crisan (Imperial College London). \n\nReferences: [1] D. Crisan\, K. L
 i\, A central limit type theorem for Gaussian mixture approximations to th
 e nonlinear filtering problem\, ArXiv1401:6592\, (2014).\n\n[2] D. Crisan\
 , K. Li\, Generalised particle filters with Gaussian mixtures\, accepted b
 y\nStochastic Processes and their Applications\, ArXiv1306:0255\, (2013).\
 n\n[3] D. Crisan\, K. Li\, Generalised particle filters with Gaussian meas
 ures\, Proceedings of\n19th European Signal Processing Conference\, Barcel
 ona\, Spain\, pp. 659-663\, (2011).\n
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR