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CATEGORIES:Isaac Newton Institute Seminar Series
SUMMARY:Generalised Particle Filters with Gaussian Mixture
 s - Li\, K (Uppsala University)
DTSTART;TZID=Europe/London:20140425T115000
DTEND;TZID=Europe/London:20140425T122500
UID:TALK52186AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/52186
DESCRIPTION:Stochastic filtering is defined as the estimation 
 of a partially observed dynamical system.\nA massi
 ve scientific and computational effort has been de
 dicated to the development of numerical methods fo
 r approximating the solution of the filtering prob
 lem. Approximating with Gaussian mixtures has been
  very popular since the 1970s\, however the existi
 ng work is only based on the success of the numeri
 cal implementation and is not theoretically justif
 ied.\n\nWe fill this gap and conduct a rigorous an
 alysis of a new Gaussian mixture approximation\nto
  the solution of the filtering problem. In particu
 lar\, we construct the corresponding approximating
  algorithm\, deduce the L2-convergence rate and pr
 ove a central limit type theorem for the approxima
 ting system. In addition\, we show a numerical exa
 mple to illustrate some features of this algorithm
 . This is joint work with Dan Crisan (Imperial Col
 lege London). \n\nReferences: [1] D. Crisan\, K. L
 i\, A central limit type theorem for Gaussian mixt
 ure approximations to the nonlinear filtering prob
 lem\, ArXiv1401:6592\, (2014).\n\n[2] D. Crisan\, 
 K. Li\, Generalised particle filters with Gaussian
  mixtures\, accepted by\nStochastic Processes and 
 their Applications\, ArXiv1306:0255\, (2013).\n\n[
 3] D. Crisan\, K. Li\, Generalised particle filter
 s with Gaussian measures\, Proceedings of\n19th Eu
 ropean Signal Processing Conference\, Barcelona\, 
 Spain\, pp. 659-663\, (2011).\n
LOCATION:Seminar Room 1\, Newton Institute
CONTACT:Mustapha Amrani
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