1 Introduction to simulation and Monte Carlo.
1.1 Evaluating a definite integral.
1.2 Monte Carlo is integral estimation.
1.3 An example.
1.4 A simulation using Maple.
2 Uniform random numbers.
2.1 Linear congruential generators.
2.2 Theoretical tests for random numbers.
2.3 Shuffled generator.
2.4 Empirical tests.
2.5 Combinations of generators.
2.6 The seed(s) in a random number generator.
3 General methods for generating random variates.
3.1 Inversion of the cumulative distribution function.
3.2 Envelope rejection.
3.3 Ratio of uniforms method.
3.4 Adaptive rejection sampling.
4 Generation of variates from standard distributions.
4.1 Standard normal distribution.
4.2 Lognormal distribution.
4.3 Bivariate normal density.
4.4 Gamma distribution.
4.5 Beta distribution.
4.6 Chi-squared distribution.
4.7 Student’s t distribution.
4.8 Generalized inverse Gaussian distribution.
4.9 Poisson distribution.
4.10 Binomial distribution.
4.11 Negative binomial distribution.
5 Variance reduction.
5.1 Antithetic variates.
5.2 Importance sampling.
5.3 Stratified sampling.
5.4 Control variates.
5.5 Conditional Monte Carlo.
6 Simulation and finance.
6.1 Brownian motion.
6.2 Asset price movements.
6.3 Pricing simple derivatives and options.
6.4 Asian options.
6.5 Basket options.
6.6 Stochastic volatility.
7 Discrete event simulation.
7.1 Poisson process.
7.2 Time-dependent Poisson process.
7.3 Poisson processes in the plane.
7.4 Markov chains.
7.5 Regenerative analysis.
7.6 Simulating a G/G/1 queueing system using the three-phase method.
7.7 Simulating a hospital ward.
8 Markov chain Monte Carlo.
8.1 Bayesian statistics.
8.2 Markov chains and the Metropolis–Hastings (MH) algorithm.
8.3 Reliability inference using an independence sampler.
8.4 Single component Metropolis–Hastings and Gibbs sampling.
8.5 Other aspects of Gibbs sampling.
9.1 Solutions 1.
9.2 Solutions 2.
9.3 Solutions 3.
9.4 Solutions 4.
9.5 Solutions 5.
9.6 Solutions 6.
9.7 Solutions 7.
9.8 Solutions 8.
Appendix 1: Solutions to problems in Chapter 1.
Appendix 2: Random Number Generators.
Appendix 3: Computations of acceptance probabilities.
Appendix 4: Random variate generators (standard distributions).
Appendix 5: Variance Reduction.
Appendix 6: Simulation and Finance.
Appendix 7: Discrete event simulation.
Appendix 8: Markov chain Monte Carlo.
"The book does a nice job of discussing, developing, and presenting the mathematical aspects of random processes, random number generation, and Markov chain Monte Carlo (MCMC) methods. I particularly like the notation used and the depth of proofs offered; they are technically correct, well organized, and nicely presented." (Journal of the American Statistical Association, June 2008)
?Dagpunar presents a textbook based on 20-hour courses he has taught for advanced students of mathematics and students of financial mathematics.? (SciTech Book Reviews, June 2007)
"?excellent for students and practitioners who don't have previous experience with simulation methods?a great contribution." (MAA Reviews, April 5, 2007)
- Provides a comprehensive introduction to simulation and Monte Carlo.
- Includes coverage of Markov Chain Monte Carlo.
- Includes algorithms displayed in pseudo-code and Maple.
- Features applications in the pricing of financial options.
- Includes exercises with solutions, encouraging use as a course text or for self-study.
- Written in a concise and lucid style suitable.
- Supported by a Website featuring all the computer code, and additional teaching material.