Variational Particle Approximation for imperfect models
نویسنده
چکیده
Whereas classical data processing techniques work with perfect models, or models with dynamical white noise, geophysical sciences have to deal with imperfect models containing spatially structured errors. For the perfect model cases, in terms of Mean-Field Markovian processes, the optimal filter is known: the Kalman estimator is the answer to the linear Gaussian problem and in the general case Particle approximations are the empirical solutions to the optimal estimator with O( 1 √ N )-type of convergences. We will present another way to decompose the Bayes rule, using an one step ahead observation. This method, very expensive with a forward/backward integration, is well adapted to the strong nonlinear
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