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Particle Approximations for a Class of Stochastic Partial Differential Equations

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Abstract

The paper presents a particle approximation for a class of nonlinear stochastic partial differential equations. The work is motivated by and applied to nonlinear filtering. The new results permit the treatment of filtering problems where the signal noise is no longer independent of the observation noise.

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  1. Department of Mathematics, Imperial College London, 180 Queen's Gate, London SW7 2AZ, England

    D. Crisan

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  1. D. Crisan
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Correspondence to D. Crisan.

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Crisan, D. Particle Approximations for a Class of Stochastic Partial Differential Equations. Appl Math Optim 54, 293–314 (2006). https://doi.org/10.1007/s00245-006-0872-3

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  • DOI: https://doi.org/10.1007/s00245-006-0872-3

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