The purpose of this handbook is to provide an accessible and comprehensive compendium of Monte Carlo techniques and related topics. It contains a mix of theory (summarized), algorithms (pseudo and actual), and applications. Since the audience is broad, the theory is kept to a minimum, this without sacrificing rigor. The book is intended to be used as an essential guide to Monte Carlo methods to quickly look up ideas, procedures, formulas, pictures, etc., rather than purely a monograph for researchers or a ...

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Handbook of Monte Carlo Methods

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The purpose of this handbook is to provide an accessible and comprehensive compendium of Monte Carlo techniques and related topics. It contains a mix of theory (summarized), algorithms (pseudo and actual), and applications. Since the audience is broad, the theory is kept to a minimum, this without sacrificing rigor. The book is intended to be used as an essential guide to Monte Carlo methods to quickly look up ideas, procedures, formulas, pictures, etc., rather than purely a monograph for researchers or a textbook for students. As the popularity of these methods continues to grow, and new methods are developed in rapid succession, the staggering number of related techniques, ideas, concepts and algorithms makes it difficult to maintain an overall picture of the Monte Carlo approach. This book attempts to encapsulate the emerging dynamics of this field of study.

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Editorial Reviews

From the Publisher
“Statisticians Kroese, Thomas Taimre (both U. of Queensland), and Zdravko I. Botev (U. of Montreal)

offer researchers and graduate and advanced graduate students a compendium of Monte Carlo

methods, which are statistical methods that involve random experiments on a computer. There are a

great many such methods being used for so many kinds of problems in so many fields that such an

overall view is hard to find. Combining theory, algorithms, and applications, they consider such topics

as uniform random number generation, probability distributions, discrete event simulation, variance

reduction, estimating derivatives, and applications to network reliability.” (Annotation ©2011 Book News

Inc. Portland, OR)

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Product Details

  • ISBN-13: 9781118014950
  • Publisher: Wiley
  • Publication date: 6/6/2013
  • Series: Wiley Series in Probability and Statistics , #706
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 772
  • File size: 32 MB
  • Note: This product may take a few minutes to download.

Meet the Author

Dirk P. Kroese, PhD, is Australian Professorial Fellow in Statistics at The University of Queensland (Australia). Dr. Kroese has more than seventy publications in such areas as stochastic modeling, randomized algorithms, computational statistics, and reliability. He is a pioneer of the cross-entropy method and the coauthor of Simulation and the Monte Carlo Method, Second Edition (Wiley).

Thomas Taimre, PhD, is a Postdoctoral Research Fellow at The University of Queensland. He currently focuses his research on Monte Carlo methods and simulation, from the theoretical foundations to performing computer implementations.

 Zdravko I. Botev, PhD, is a Postdoctoral Research Fellow at the University of Montreal  (Canada). His research interests include the splitting method for rare-event simulation and kernel density estimation. He is the author of one of the most widely used free MATLAB® statistical software programs for nonparametric kernel density estimation.

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Table of Contents



1 Uniform Random Number Generation.

1.1 Random Numbers.

1.2 Generators Based on Linear Recurrences.

1.3 Combined Generators.

1.4 Other Gnerators.

1.5 Tests for Random Number Generators.


2 Quasirandom Number Generation.

2.1 Multidimensional Integration.

2.2 Van der Corput and Digital Sequences.

2.3 Halton Sequences.

2.4 Faure Sequences.

2.5 Sobol’ Sequences.

2.6 Lattice Methods.

2.7 Randomization and Scrambling.


3 Random Variable Generation.

3.1 Generic Algorithms Based on Common Transformations.

3.2 Copulas.

3.3 Generation Methods for Various Random Objects.


4 Probability Distributions.

4.1 Discrete Distributions.

4.2 Continuous Distributions.

4.3 Multivariate Distributions.


5 Random Process Generation.

5.1 Gaussian Processes.

5.2 Markov Chains.

5.3 Markov Jump Processes.

5.4 Poisson Processes.

5.5 Wiener Process and Brownian Motion.

5.6 Stochastic Differential Equations and Diffusion Processes.

5.7 Brownian Bridge.

5.8 Geometric Brownian Motion.

5.9 Ornstein-Uhlenbeck Process.

5.10 Reflected Brownian Motion.

5.11 Fractional Brownian Motion.

5.12 Random Fields.

5.13 Lévy Processes.

5.14 Time Series.


6 Markov Chain Monte Carlo.

6.1 Metropolis-Hastings Algorithm.

6.2 Gibbs Sampler.

6.3 Specialized Samplers.

6.4 Implementation Issues.

6.5 Perfect Sampling.


7 Discrete Event Simulation.

7.1 Simulation Models.

7.2 Discrete Event Systems.

7.3 Event-Oriented Approach.

7.4 More Examples of Discrete Event Simulation.


8 Statistical Analysis of Simulation Data.

8.1 Simulation Data.

8.2 Estimation of Performance Measures for Independent Data.

8.3 Estimation of Steady-State Performance Measures.

8.4 Emprical Cdf.

8.5 Kernal Density Estimation.

8.6 Resampling and the Bootstrap Method.

8.7 Goodness of Fit.


9 Variance Reduction.

9.1 Variance Reduction Example.

9.2 Antithetic Random Variables.

9.3 Control Variables.

9.4 Conditional Monte Carlo.

9.5 Stratified Sampling.

9.6 Latin Hypercube Sampling.

9.7 Importance Scaling.

9.8 Quasi Monte Carlo


10 Rare-Event Simulation.

10.1 Efficiency of Estimators.

10.2 Importance Sampling Methods for Light Tails.

10.3 Conditioning Methods for Heavy Tails.

10.4 State-Dependent Importance Sampling.

10.5 Cross-Entropy Method for Rare-Event Simulation.

10.6 Splitting Method.


11 Estimation of Derivatives.

11.1 Gradient Estimation.

11.2 Finite Difference Method.

11.3 Infinitesimal Perturbation Analysis.

11.4 Score Function Method.

11.5 Weak Deriatives.

11.6 Sensitivity Analysis for Regenerative Processes.


12 Randomized Optimization.

12.1 Stochastic Approximation.

12.2 Stochastic Counterpart Method.

12.3 Simulated Annealing.

12.4 Evolutionary Algorithms.

12.5 Cross-Entropy Method for Optimization.

12. 6 Other Randomized Optimization Techniques.


13 Cross-Entropy Method.

13.1 Cross-Entropy Method.

13.2 Cross-Entropy Method for Estimation.

13.3 Cross-Entropy Method for Optimization.


14 Particle Methods.

14.1 Sequential Monte Carlo.

14.2 Particle Splitting.

14.3 Splitting for Static Rare-Event Probability Estimaton.

14.4 Adaptive Splitting Algorithm.

14.5 Estimation of Multidimensional Integrals.

14.6 Combinatorial Optimization via Splitting.

14.7 Markov Chain Monte Carlo With Splitting.


15 Applications to Finance.

15.1 Standard Model.

15.2 Pricing via Monte Carlo Simulation.

15.3 Sensitivities.


16 Applications to Network  Reliability.

16.1 Network Reliability.

16.2 Evolution Model for a Static Network.

16.3 Conditional Monte Carlo.

16.4 Importance Sampling for Network Reliability.

16.5 Splitting Method.


17 Applications to Differential Equations.

17. 1 Connections Between Stochastic and Partial Di_erential Equations.

17.2 Transport Processes and Equations.

17.3 Connections to ODEs Through Scaling.


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.


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