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SUMMARY:Sequential Monte Carlo and particle methods in inverse problems - 
 Calvetti\, D (Case Western Reserve University)
DTSTART:20140211T094500Z
DTEND:20140211T103000Z
UID:TALK50802@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Andrea Arnold (CWRU)\, Erkki Somersalo (CWRU) \n\n
 In sequential Monte Carlo methods\, the posterior distribution of an unkno
 wn of interest is explored in a sequential manner\, by updating the Monte 
 Carlo sample as new data arrive. In a similar fashion\, particle filtering
  encompasses different sampling techniques to track the time course of a p
 robability density that evolves in time based on partial observations of i
 t. Methods that combine particle filters and sequential Monte Carlo have b
 een developed for some time\, mostly in connection with estimating unknown
  parameters in stochastic differential equations. In this talk\, we presen
 t some new ideas suitable for treating large scale\, non-stochastic\, seve
 rely stiff systems of differential equations combining sequential Monte Ca
 rlo methods with classical numerical analysis concepts.\n
LOCATION:Seminar Room 1\, Newton Institute
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