Strategies for Quasi-Monte Carlo

Overview

Strategies for Quasi-Monte Carlo builds a framework to design and analyze strategies for randomized quasi-Monte Carlo (RQMC). One key to efficient simulation using RQMC is to structure problems to reveal a small set of important variables, their number being the effective dimension, while the other variables collectively are relatively insignificant. Another is smoothing. Both keys get many illustrations, in particular for problems involving Poisson processes or Gaussian processes.. "This monograph has been ...
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Paperback (Softcover reprint of the original 1st ed. 1999)
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Overview

Strategies for Quasi-Monte Carlo builds a framework to design and analyze strategies for randomized quasi-Monte Carlo (RQMC). One key to efficient simulation using RQMC is to structure problems to reveal a small set of important variables, their number being the effective dimension, while the other variables collectively are relatively insignificant. Another is smoothing. Both keys get many illustrations, in particular for problems involving Poisson processes or Gaussian processes.. "This monograph has been designed to appeal to a diverse audience, including those with applications in queuing, operations research, computational finance, mathematical programming, partial differential equations (both deterministic and stochastic), or particle transport as well as to probabilists and statisticians wanting to see how to apply effectively a powerful tool and to those interested in numerical integration or optimization in their own right. While not primarily a textbook, it is suitable as a supplementary text for certain graduate courses. As a reference, it belongs on the shelf of everyone with a serious interest in improving simulation efficiency. Moreover, it will be a valuable reference to all those individuals interested in improving simulation efficiency with more than incremental increases.
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Product Details

Table of Contents

Preface. Acknowledgements. 1. Introduction. 2. Smoothing. 3. Generating Poisson Processes. 4. Permuting Order Statistics. 5. Generating Bernoulli Trials. 6. Generating Gaussian Processes. 7. Smoothing Summation. 8. Smoothing Variate Generation. 9. Analysis of Variance. 10. Bernoulli Trials: Examples. 11. Poisson Processes: Auxiliary Matter. 12. Background on Deterministic QMC. 13. Optimization. 14. Background on Randomized QMC. 15. Pseudocodes. Bibliography. Index.

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