![]() |
COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. | ![]() |
University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Nonlinear filtering algorithms based on averaging over characteristics and on the innovation approach.
Nonlinear filtering algorithms based on averaging over characteristics and on the innovation approach.Add to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Mustapha Amrani. Stochastic Partial Differential Equations (SPDEs) It is well known that numerical methods for nonlinear filtering problems, which directly use the Kallianpur-Striebel formula, can exhibit computational instabilities due to the presence of very large or very small exponents in both the numerator and denominator of the formula. We obtain computationally stable schemes by exploiting the innovation approach. We propose Monte Carlo algorithms based on the method of characteristics for linear parabolic stochastic partial differential equations. Convergence and some properties of the considered algorithms are studied. Variance reduction techniques are discussed. Results of some numerical experiments are presented. The talk is based on a joint work with G.N. Milstein. This talk is part of the Isaac Newton Institute Seminar Series series. This talk is included in these lists:
Note that ex-directory lists are not shown. |
Other listsMathematics at Work Whiston Society British Computer Society SPA CambridgeOther talksHow language variation contributes to reading difficulties and “achievement gaps” Predictive modeling of hydrogen assisted cracking – a Micromechanics conquest Cohomology of the moduli space of curves Propaganda porcelain: The mirror of the Russian revolution and its consequences Mysteries of the solar chromosphere explored using the high-resolution observations Cancer and Metbolism 2018 |