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

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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 socalled WienerPoisson 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 continuousdiscrete nonlinear 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Status:  Not Peer Reviewed 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:  MTM200806660 (Spain) 
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URI:  http://wrap.warwick.ac.uk/id/eprint/35220 
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