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Thesis

Nested multilevel Monte Carlo methods and a modified Euler-Maruyama scheme utilising approximate Gaussian random variables suitable for vectorised hardware and low-precisions

Abstract:

We present a modified Euler-Maruyama scheme using approximate random variables, produced by the inverse transform method, using cheap approximations to the inverse Gaussian cumulative distribution function. We analyse the error for two approximations: a piecewise constant approximation on equally spaced intervals, and a piecewise linear approximation using geometric intervals dense at the singularities. High speed implementations faster than Intel's MKL are provided, suitable for modern ve...

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  • (Dissemination version, Version of record, 3.5MB)

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Division:
MPLS
Department:
Mathematical Institute
Role:
Author

Contributors

Role:
Supervisor
Role:
Supervisor
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Funding agency for:
Sheridan-Methven, O
Grant:
EP/P020720/1
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
Deposit date:
2020-12-23

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