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.
Chorin
Alexandre J
AJ
Department of Mathematics, University of California, Berkeley, CA 94720, USA. chroin@math.berkeley.edu
Krause
Paul
P