More and more of today's numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field.

The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including:

Random variable and stochastic process generation
Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run
Discrete-event simulation
Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation
Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo
Estimation of derivatives and sensitivity analysis
Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization

The presented theoretical concepts are illustrated with worked examples that use MATLAB ®, a related Web site houses the MATLAB ® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation.

Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.

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Yes, you can access Handbook of Monte Carlo Methods by Dirk P. Kroese, Thomas Taimre, Zdravko I. Botev in PDF and/or ePUB format, as well as other popular books in Mathematik & Wahrscheinlichkeitsrechnung & Statistiken. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Wiley

Year

2013

ISBN

9781118014950

Edition

Topic

Mathematik

Subtopic

Wahrscheinlichkeitsrechnung & Statistiken

Cover
Contents
Title page
Copyright
Dedication
Preface
Acknowledgments
CHAPTER 1: Uniform Random Number Generation
CHAPTER 2: Quasirandom Number Generation
CHAPTER 3: Random Variable Generation
CHAPTER 4: Probability Distributions
CHAPTER 5: Random Process Generation
CHAPTER 6: Markov Chain Monte Carlo
CHAPTER 7: Discrete Event Simulation
CHAPTER 8: Statistical Analysis of Simulation Data
CHAPTER 9: Variance Reduction
CHAPTER 10: Rare-Event Simulation
CHAPTER 11: Estimation of Derivatives
CHAPTER 12: Randomized Optimization
CHAPTER 13: Cross-Entropy Method
CHAPTER 14: Particle Methods
CHAPTER 15: Applications to Finance
CHAPTER 16: Applications to Network Reliability
CHAPTER 17: Applications to Differential Equations
Appendix A: Probability and Stochastic Processes
Appendix B: Elements of Mathematical Statistics
Appendix C: Optimization
Appendix D: Miscellany
Acronyms and Abbreviations
List of Symbols
List of Distributions
Index