INDICE: Preface. Acknowledgments. 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. References.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. References. 3. Random Variable Generation. 3.1 Generic Algorithms Based on Common Transformations. 3.2 Copulas. 3.3 Generation Methods for Various Random Objects. References. 4. Probability Distributions. 4.1 Discrete Distributions. 4.2 Continuous Distribution. 4.3 Multivariate Distribution. References. 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 Di_erential Equations and Di_usion Processes. 5.7 Brownian Bridge. 5.8 Geometric Brownian Motion. 5.9 Ornstein{Uhlenbeck Process. 5.10 Reected Brownian Motion. 5.11 Fractional Brownian Motion. 5.12 Random Fields. 5.13 L_evyProcesses. 5.14 Time Series. References. 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. References. 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. References. 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 Resamplingand the Bootstrap Method. 8.7 Goodness of Fit. References. 9. Variance Reduction. 9.1 Variance Reduction Example. 9.2 Antithetic Random Variables. 9.3 Control Variables. 9.4 Conditional Monte Carlo. 9.5 Strati_ed Sampling. 9.6 Latin Hypercube Sampling. 9.7 Minimum-Variance Density. 9.8 Quasi Monte Carlo References. 10. Rare-Event Simulation. 10.1 E_ciency Sampling. 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. References. 11. Estimation of Derivatives. 11.1 Gradient Estimation. 11.2 Finite Di_erence Method. 11.3 In_nitesimal Perturbation Analysis. 11.4 Score Function Method. 11.5 Weak Deriatives. 11.6 Sensitivity Analysis for Regenerative Processes. References. 12. Randomized Optimization. 12.1 Stochastic Approximation. 12.2 Stochastic Counterpart Method. 12.3Simulated Annealing. 12.4 Evolutionary Algorithms. 12.5 Cross-Entropy Method for Optim
- ISBN: 978-0-470-17793-8
- Editorial: John Wiley & Sons
- Encuadernacion: Cartoné
- Páginas: 743
- Fecha Publicación: 18/02/2011
- Nº Volúmenes: 1
- Idioma: Inglés