Automatic Nonuniform Random Variate Generation

1. Auflage. Softcover version of original hardcover Auflage 2004
ISBN: 978-3-642-07372-4
Verlag: Springer

Buch, Englisch, 441 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 680 g

Reihe: Statistics and Computing

Non-uniform random variate generation is an established research area in the intersection of mathematics, statistics and computer science. Although random variate generation with popular standard distributions have become part of every course on discrete event simulation and on Monte Carlo methods, the recent concept of universal (also called automatic or black-box) random variate generation can only be found dispersed in literature. This new concept has great practical advantages that are little known to most simulation practitioners. Being unique in its overall organization the book covers not only the mathematical and statistical theory, but also deals with the implementation of such methods. All algorithms introduced in the book are designed for practical use in simulation and have been coded and made available by the authors. Examples of possible applications of the presented algorithms (including option pricing, VaR and Bayesian statistics) are presented at the end of the book.

Zielgruppe

Research

Autoren/Hrsg.

Hörmann, Wolfgang

Derflinger, Gerhard

Leydold, Josef

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

1 Introduction.- 2 General Principles in Random Variate Generation.- 3 General Principles for Discrete Distributions.- 4 Transformed Density Rejection (TDR).- 5 Strip Methods.- 6 Methods Based on General Inequalities.- 7 Numerical Inversion.- 8 Comparison and General Considerations.- 9 Distributions Where the Density Is Not Known Explicitly.- 10 Discrete Distributions.- 11 Multivariate Distributions.- 12 Combination of Generation and Modeling.- 13 Time Series (Authors Michael Hauser and Wolfgang Hörmann).- 14 Markov Chain Monte Carlo Methods.- 15 Some Simulation Examples.- List of Algorithms.- References.- Author index.- Selected Notation.- Subject Index and Glossary.