The Annals of Applied Probability
- Ann. Appl. Probab.
- Volume 25, Number 5 (2015), 2809-2866.
Can local particle filters beat the curse of dimensionality?
The discovery of particle filtering methods has enabled the use of nonlinear filtering in a wide array of applications. Unfortunately, the approximation error of particle filters typically grows exponentially in the dimension of the underlying model. This phenomenon has rendered particle filters of limited use in complex data assimilation problems. In this paper, we argue that it is often possible, at least in principle, to develop local particle filtering algorithms whose approximation error is dimension-free. The key to such developments is the decay of correlations property, which is a spatial counterpart of the much better understood stability property of nonlinear filters. For the simplest possible algorithm of this type, our results provide under suitable assumptions an approximation error bound that is uniform both in time and in the model dimension. More broadly, our results provide a framework for the investigation of filtering problems and algorithms in high dimension.
Ann. Appl. Probab., Volume 25, Number 5 (2015), 2809-2866.
Received: March 2013
Revised: May 2014
First available in Project Euclid: 30 July 2015
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Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 60G35: Signal detection and filtering [See also 62M20, 93E10, 93E11, 94Axx] 60K35: Interacting random processes; statistical mechanics type models; percolation theory [See also 82B43, 82C43] 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11] 65C05: Monte Carlo methods 68Q87: Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) [See also 68W20, 68W40]
Rebeschini, Patrick; van Handel, Ramon. Can local particle filters beat the curse of dimensionality?. Ann. Appl. Probab. 25 (2015), no. 5, 2809--2866. doi:10.1214/14-AAP1061. https://projecteuclid.org/euclid.aoap/1438261054