Particle Approximations for a Class of Stochastic Partial Differential Equations

Particle Approximations for a Class of Stochastic Partial Differential Equations

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 n...

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Veröffentlicht in:Applied mathematics & optimization 2006-11, Vol.54 (3), p.293-314
1. Verfasser: Crisan, D.
Format: Artikel
Sprache:eng
Schlagworte:
Approximation
APPROXIMATIONS
Brownian motion
CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS
Euclidean space
NOISE
Nonlinear equations
NONLINEAR PROBLEMS
PARTIAL DIFFERENTIAL EQUATIONS
SIGNALS
Stochastic models
STOCHASTIC PROCESSES
Studies
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Zusammenfassung: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. [PUBLICATION ABSTRACT]
ISSN:0095-4616
1432-0606
DOI:10.1007/s00245-006-0872-3