Papaspiliopoulos, Omiros (2009) A methodological framework for Monte Carlo probabilistic inference for diffusion processes. Working Paper. University of Warwick. Centre for Research in Statistical Methodology, Coventry.

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Abstract

The methodological framework developed and reviewed in this article concerns the unbiased Monte Carlo estimation of the transition density of a diffusion process, and the exact simulation of diffusion processes. The former relates to auxiliary variable methods, and it builds on a rich generic Monte Carlo machinery of unbiased estimation and simulation of infinite series expansions which relates to techniques used in diverse scientific areas such as population genetics and operational research. The latter is a recent significant advance in the numerics for diffusions, it is based on the so-called Wiener-Poisson factorization of the diffusion measure, and it has interesting connections to exact simulation of killing times for the Brownian motion and interacting particle systems, which are uncovered in this article. A concrete application to probabilistic inference for diffusion processes is presented by considering the continuous-discrete non-linear filtering problem.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Monte Carlo method, Diffusion processes
Series Name:	Working papers
Publisher:	University of Warwick. Centre for Research in Statistical Methodology
Place of Publication:	Coventry
Date:	2009
Volume:	Vol.2009
Number:	No.31
Number of Pages:	24
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Spain
Grant number:	MTM2008-06660 (Spain)
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URI:	http://wrap.warwick.ac.uk/id/eprint/35220

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