Error reduction techniques in quasi-monte carlo integration

Authors:
G. Ökten
Affiliations:
-
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1999

Citing 3
Cited 3

On effective computation of expectations in large or infinite dimension

Journal of Computational and Applied Mathematics - Random numbers and simulation
Implementation and tests of low-discrepancy sequences

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Variants of the Koksma-Hlawka inequality for vertex-modified quasi-Monte Carlo integration rules

Mathematical and Computer Modelling: An International Journal

Some current issues in quasi-Monte Carlo methods

Journal of Complexity
On the convergence of quasi-random sampling/importance resampling

Mathematics and Computers in Simulation
Weighted geometric discrepancies and numerical integration on reproducing kernel Hilbert spaces

Journal of Complexity

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Abstract

A generalized quasi-Monte Carlo integration rule is introduced. A Koksma-Hlawka type inequality for the rule is proved, using a recently introduced concept ''bounded variation in the measure sense''. Error reduction techniques and, in particular, ''importance sampling'' are studied as the consequences of the integration rule. (C) 1999 Elsevier Science Ltd. All rights reserved.