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On Markov Chain Approximations to Semilinear Partial Differential Equations Driven by Poisson Measure Noise 

Authors: Michael A. Kouritzin a;  Hongwei Long a; Wei Sun a
Affiliation:   a Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada
DOI: 10.1081/SAP-120019293
Publication Frequency: 6 issues per year
Published in: journal Stochastic Analysis and Applications, Volume 21, Issue 2 January 2003 , pages 419 - 441
Formats available: HTML (English) : PDF (English)
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Abstract

We consider the stochastic model of water pollution, which mathematically can be written with a stochastic partial differential equation driven by Poisson measure noise. We use a stochastic particle Markov chain method to produce an implementable approximate solution. Our main result is the annealed law of large numbers establishing convergence in probability of our Markov chains to the solution of the stochastic reaction-diffusion equation while considering the Poisson source as a random medium for the Markov chains.
Keywords: Stochastic reaction diffusion equations; Markov chains; Poisson processes; Annealed law of large numbers
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