{"product_id":"handbook-of-monte-carlo-methods-isbn-9780470177938","title":"Handbook of Monte Carlo Methods","description":"\u003cb\u003eA comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications\u003c\/b\u003e  \u003cp\u003eMore 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. \u003ci\u003eHandbook of Monte Carlo Methods\u003c\/i\u003e provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field.\u003c\/p\u003e \u003cp\u003eThe 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:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003eRandom variable and stochastic process generation\u003c\/li\u003e \u003cli\u003eMarkov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run\u003c\/li\u003e \u003cli\u003eDiscrete-event simulation\u003c\/li\u003e \u003cli\u003eTechniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation\u003c\/li\u003e \u003cli\u003eVariance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo\u003c\/li\u003e \u003cli\u003eEstimation of derivatives and sensitivity analysis\u003c\/li\u003e \u003cli\u003eAdvanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe presented theoretical concepts are illustrated with worked examples that use MATLAB\u003csup\u003e®\u003c\/sup\u003e, a related Web site houses the MATLAB\u003csup\u003e®\u003c\/sup\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHandbook of Monte Carlo Methods\u003c\/i\u003e 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.\u003c\/p\u003e  Preface.  \u003cp\u003eAcknowledgments.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Uniform Random Number Generation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Random Numbers.\u003c\/p\u003e \u003cp\u003e1.2 Generators Based on Linear Recurrences.\u003c\/p\u003e \u003cp\u003e1.3 Combined Generators.\u003c\/p\u003e \u003cp\u003e1.4 Other Gnerators.\u003c\/p\u003e \u003cp\u003e1.5 Tests for Random Number Generators.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Quasirandom Number Generation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Multidimensional Integration.\u003c\/p\u003e \u003cp\u003e2.2 Van der Corput and Digital Sequences.\u003c\/p\u003e \u003cp\u003e2.3 Halton Sequences.\u003c\/p\u003e \u003cp\u003e2.4 Faure Sequences.\u003c\/p\u003e \u003cp\u003e2.5 Sobol’ Sequences.\u003c\/p\u003e \u003cp\u003e2.6 Lattice Methods.\u003c\/p\u003e \u003cp\u003e2.7 Randomization and Scrambling.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Random Variable Generation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Generic Algorithms Based on Common Transformations.\u003c\/p\u003e \u003cp\u003e3.2 Copulas.\u003c\/p\u003e \u003cp\u003e3.3 Generation Methods for Various Random Objects.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Probability Distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Discrete Distributions.\u003c\/p\u003e \u003cp\u003e4.2 Continuous Distributions.\u003c\/p\u003e \u003cp\u003e4.3 Multivariate Distributions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Random Process Generation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Gaussian Processes.\u003c\/p\u003e \u003cp\u003e5.2 Markov Chains.\u003c\/p\u003e \u003cp\u003e5.3 Markov Jump Processes.\u003c\/p\u003e \u003cp\u003e5.4 Poisson Processes.\u003c\/p\u003e \u003cp\u003e5.5 Wiener Process and Brownian Motion.\u003c\/p\u003e \u003cp\u003e5.6 Stochastic Differential Equations and Diffusion Processes.\u003c\/p\u003e \u003cp\u003e5.7 Brownian Bridge.\u003c\/p\u003e \u003cp\u003e5.8 Geometric Brownian Motion.\u003c\/p\u003e \u003cp\u003e5.9 Ornstein-Uhlenbeck Process.\u003c\/p\u003e \u003cp\u003e5.10 Reflected Brownian Motion.\u003c\/p\u003e \u003cp\u003e5.11 Fractional Brownian Motion.\u003c\/p\u003e \u003cp\u003e5.12 Random Fields.\u003c\/p\u003e \u003cp\u003e5.13 Lévy Processes.\u003c\/p\u003e \u003cp\u003e5.14 Time Series.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Markov Chain Monte Carlo.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Metropolis-Hastings Algorithm.\u003c\/p\u003e \u003cp\u003e6.2 Gibbs Sampler.\u003c\/p\u003e \u003cp\u003e6.3 Specialized Samplers.\u003c\/p\u003e \u003cp\u003e6.4 Implementation Issues.\u003c\/p\u003e \u003cp\u003e6.5 Perfect Sampling.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Discrete Event Simulation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Simulation Models.\u003c\/p\u003e \u003cp\u003e7.2 Discrete Event Systems.\u003c\/p\u003e \u003cp\u003e7.3 Event-Oriented Approach.\u003c\/p\u003e \u003cp\u003e7.4 More Examples of Discrete Event Simulation.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Statistical Analysis of Simulation Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Simulation Data.\u003c\/p\u003e \u003cp\u003e8.2 Estimation of Performance Measures for Independent Data.\u003c\/p\u003e \u003cp\u003e8.3 Estimation of Steady-State Performance Measures.\u003c\/p\u003e \u003cp\u003e8.4 Emprical Cdf.\u003c\/p\u003e \u003cp\u003e8.5 Kernal Density Estimation.\u003c\/p\u003e \u003cp\u003e8.6 Resampling and the Bootstrap Method.\u003c\/p\u003e \u003cp\u003e8.7 Goodness of Fit.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Variance Reduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Variance Reduction Example.\u003c\/p\u003e \u003cp\u003e9.2 Antithetic Random Variables.\u003c\/p\u003e \u003cp\u003e9.3 Control Variables.\u003c\/p\u003e \u003cp\u003e9.4 Conditional Monte Carlo.\u003c\/p\u003e \u003cp\u003e9.5 Stratified Sampling.\u003c\/p\u003e \u003cp\u003e9.6 Latin Hypercube Sampling.\u003c\/p\u003e \u003cp\u003e9.7 Importance Scaling.\u003c\/p\u003e \u003cp\u003e9.8 Quasi Monte Carlo\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Rare-Event Simulation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Efficiency of Estimators.\u003c\/p\u003e \u003cp\u003e10.2 Importance Sampling Methods for Light Tails.\u003c\/p\u003e \u003cp\u003e10.3 Conditioning Methods for Heavy Tails.\u003c\/p\u003e \u003cp\u003e10.4 State-Dependent Importance Sampling.\u003c\/p\u003e \u003cp\u003e10.5 Cross-Entropy Method for Rare-Event Simulation.\u003c\/p\u003e \u003cp\u003e10.6 Splitting Method.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Estimation of Derivatives.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Gradient Estimation.\u003c\/p\u003e \u003cp\u003e11.2 Finite Difference Method.\u003c\/p\u003e \u003cp\u003e11.3 Infinitesimal Perturbation Analysis.\u003c\/p\u003e \u003cp\u003e11.4 Score Function Method.\u003c\/p\u003e \u003cp\u003e11.5 Weak Deriatives.\u003c\/p\u003e \u003cp\u003e11.6 Sensitivity Analysis for Regenerative Processes.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Randomized Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Stochastic Approximation.\u003c\/p\u003e \u003cp\u003e12.2 Stochastic Counterpart Method.\u003c\/p\u003e \u003cp\u003e12.3 Simulated Annealing.\u003c\/p\u003e \u003cp\u003e12.4 Evolutionary Algorithms.\u003c\/p\u003e \u003cp\u003e12.5 Cross-Entropy Method for Optimization.\u003c\/p\u003e \u003cp\u003e12. 6 Other Randomized Optimization Techniques.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Cross-Entropy Method.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Cross-Entropy Method.\u003c\/p\u003e \u003cp\u003e13.2 Cross-Entropy Method for Estimation.\u003c\/p\u003e \u003cp\u003e13.3 Cross-Entropy Method for Optimization.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Particle Methods.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Sequential Monte Carlo.\u003c\/p\u003e \u003cp\u003e14.2 Particle Splitting.\u003c\/p\u003e \u003cp\u003e14.3 Splitting for Static Rare-Event Probability Estimaton.\u003c\/p\u003e \u003cp\u003e14.4 Adaptive Splitting Algorithm.\u003c\/p\u003e \u003cp\u003e14.5 Estimation of Multidimensional Integrals.\u003c\/p\u003e \u003cp\u003e14.6 Combinatorial Optimization via Splitting.\u003c\/p\u003e \u003cp\u003e14.7 Markov Chain Monte Carlo With Splitting.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Applications to Finance.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Standard Model.\u003c\/p\u003e \u003cp\u003e15.2 Pricing via Monte Carlo Simulation.\u003c\/p\u003e \u003cp\u003e15.3 Sensitivities.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Applications to Network  Reliability.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Network Reliability.\u003c\/p\u003e \u003cp\u003e16.2 Evolution Model for a Static Network.\u003c\/p\u003e \u003cp\u003e16.3 Conditional Monte Carlo.\u003c\/p\u003e \u003cp\u003e16.4 Importance Sampling for Network Reliability.\u003c\/p\u003e \u003cp\u003e16.5 Splitting Method.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Applications to Differential Equations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17. 1 Connections Between Stochastic and Partial Di_erential Equations.\u003c\/p\u003e \u003cp\u003e17.2 Transport Processes and Equations.\u003c\/p\u003e \u003cp\u003e17.3 Connections to ODEs Through Scaling.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Probability and Stochastic Processes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: Elements of Mathematical Statistics.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C: Optimization.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D: Miscellany.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eAcronyms and Abbreviations.\u003c\/p\u003e \u003cp\u003eList of Symbols.\u003c\/p\u003e \u003cp\u003eList of Distributions.\u003c\/p\u003e \u003cp\u003eIndex. \u003c\/p\u003e  \u003cspan style=\"font-family: \" helvetica color:=\"\" font-size:=\"\"\u003e“Statisticians Kroese, Thomas Taimre (both U. of Queensland), and Zdravko I. Botev (U. of Montreal)\u003c\/span\u003e\u003cbr\u003e \u003cbr\u003e   \u003cp\u003e\u003cspan style=\"font-family: \" helvetica color: font-size:\u003eoffer researchers and graduate and advanced graduate students a compendium of Monte Carlo\u003c\/span\u003e\u003c\/p\u003e \u003cp\u003e\u003cspan style=\"font-family: \" helvetica color: font-size:\u003emethods, which are statistical methods that involve random experiments on a computer. There are a\u003c\/span\u003e\u003c\/p\u003e \u003cp\u003e\u003cspan style=\"font-family: \" helvetica color: font-size:\u003egreat many such methods being used for so many kinds of problems in so many fields that such an\u003c\/span\u003e\u003c\/p\u003e \u003cp\u003e\u003cspan style=\"font-family: \" helvetica color: font-size:\u003eoverall view is hard to find. Combining theory, algorithms, and applications, they consider such topics\u003c\/span\u003e\u003c\/p\u003e \u003cp\u003e\u003cspan style=\"font-family: \" helvetica color: font-size:\u003eas uniform random number generation, probability distributions, discrete event simulation, variance\u003c\/span\u003e\u003c\/p\u003e \u003cp\u003e\u003cspan style=\"font-family: \" helvetica color: font-size:\u003ereduction, estimating derivatives, and applications to network reliability.” (Annotation ©2011 Book News\u003c\/span\u003e\u003c\/p\u003e \u003cp\u003e\u003cspan style=\"line-height: 115%; font-family: \" helvetica color: font-size:\u003eInc. Portland, OR)\u003c\/span\u003e\u003c\/p\u003e  \u003cb\u003eDirk P. Kroese, PhD,\u003c\/b\u003e 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).  \u003cp\u003e\u003cb\u003eThomas Taimre, PhD,\u003c\/b\u003e 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.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e Zdravko I. Botev, PhD\u003c\/b\u003e, 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.\u003c\/p\u003e  \u003cb\u003eA comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications\u003c\/b\u003e  \u003cp\u003eMore 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. \u003ci\u003eHandbook of Monte Carlo Methods\u003c\/i\u003e provides the theory, algorithms, and applications that facilitate a thorough understanding of the emerging dynamics of this rapidly growing field.\u003c\/p\u003e \u003cp\u003eThe 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cdiv\u003eRandom variable and stochastic process generation\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eMarkov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eDiscrete-event simulation\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eTechniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eVariance reduction, including importance sampling, Latin hypercube sampling, and conditional Monte Carlo\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eEstimation or derivatives and sensitivity analysis\u003c\/div\u003e \u003c\/li\u003e \u003cli\u003e \u003cdiv\u003eAdvanced topics including cross-entropy, rare events, kernel density estimation, quasi-Monte Carlo, particle systems, and randomized optimization\u003c\/div\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe presented theoretical concepts are illustrated with worked examples that use MATLAB®. A related website 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 on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that ate relevant to Monte Carlo simulation.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eHandbook of Monte Carlo Methods\u003c\/i\u003e 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 as the upper-undergraduate and graduate levels. \u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989337161957,"sku":"NP9780470177938","price":185.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470177938.jpg?v=1761783719","url":"https:\/\/k12savings.com\/es\/products\/handbook-of-monte-carlo-methods-isbn-9780470177938","provider":"K12savings","version":"1.0","type":"link"}