Dimensional reduction for a Bayesian filter.
An adaptive strategy is proposed for reducing the number of unknowns in the calculation of a proposal distribution in a sequential Monte Carlo implementation of a Bayesian filter for nonlinear dynamics. The idea is to solve only in directions in which the dynamics is expanding, found adaptively; this strategy is suggested by earlier work on optimal prediction. The construction should be of value in data assimilation, for example, in geophysical fluid dynamics.
Department of Mathematics, University of California, Berkeley, CA 94720, USA. chroin@math.berkeley.edu
Alexandre J
AJ
Chorin
Paul
P
Krause