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A Distributed Procedure for Computing Stochastic Expansions with Mathematica

Christophe Ladroue
Organization: University of Warwick
Department: Department of Statistics
Anastasia Papavasiliou
Journal / Anthology

Journal of Statistical Software
Year: 2013
Volume: 53
Issue: 11
Page range: 1-15

The solution of a (stochastic) di erential equation can be locally approximated by a (stochastic) expansion. If the vector eld of the di erential equation is a polynomial, the corresponding expansion is a linear combination of iterated integrals of the drivers and can be calculated using Picard Iterations. However, such expansions grow exponentially fast in their number of terms, due to their speci c algebra, rendering their practical use limited. We present a Mathematica procedure that addresses this issue by reparametrizing the polynomials and distributing the load in as small as possible parts that can be processed and manipulated independently, thus alleviating large memory requirements and being perfectly suited for parallelized computation. We also present an iterative implementation of the shue product (as opposed to a recursive one, more usually implemented) as well as a fast way for calculating the expectation of iterated Stratonovich integrals for Brownian motion.

*Mathematics > Probability and Statistics

rough paths, stochastic expansion, iterated integral, picard iteration, simulation, Mathematica.

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