caa a22 4500 467892199 CHVBK 20180406152752.0 cr unu---uuuuu 170328e20060601xx s 000 0 eng 10.1007/s10955-006-9124-9 doi (NATIONALLICENCE)springer-10.1007/s10955-006-9124-9 A Maximum Entropy Method for Particle Filtering [Elektronische Daten] [Gregory Eyink, Sangil Kim] Standard ensemble or particle filtering schemes do not properly represent states of low priori probability when the number of available samples is too small, as is often the case in practical applications. We introduce here a set of parametric resampling methods to solve this problem. Motivated by a general H-theorem for relative entropy, we construct parametric models for the filter distributions as maximum-entropy/minimum-information models consistent with moments of the particle ensemble. When the prior distributions are modeled as mixtures of Gaussians, our method naturally generalizes the ensemble Kalman filter to systems with highly non-Gaussian statistics. We apply the new particle filters presented here to two simple test cases: a one-dimensional diffusion process in a double-well potential and the three-dimensional chaotic dynamical system of Lorenz. Springer Science + Business Media, Inc., 2006 Bayesian estimation nationallicence filtering nationallicence particle methods nationallicence maximum-entropy nationallicence mixture models nationallicence ensemble Kalman filter nationallicence Eyink Gregory Department of Applied Mathematics and Statistics, The Johns Hopkins University, 21218, Baltimore, MD, U.S.A. aut Kim Sangil Department of Mathematics, University of Arizona, 85721, Tucson, AZ, U.S.A. aut Journal of Statistical Physics Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 123/5(2006-06-01), 1071-1128 0022-4715 123:5<1071 2006 123 10955 https://doi.org/10.1007/s10955-006-9124-9 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10955-006-9124-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Eyink Gregory Department of Applied Mathematics and Statistics, The Johns Hopkins University, 21218, Baltimore, MD, U.S.A aut NATIONALLICENCE 700 1- Kim Sangil Department of Mathematics, University of Arizona, 85721, Tucson, AZ, U.S.A aut NATIONALLICENCE 773 0- Journal of Statistical Physics Kluwer Academic Publishers-Plenum Publishers; http://www.springer-ny.com 123/5(2006-06-01), 1071-1128 0022-4715 123:5<1071 2006 123 10955 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer