Rubinstein is the pioneer of the well-known score function and cross-entropy methods. Accessible to a broad audience of engineers, computer scientists, mathematicians, statisticians and in general anyone, theorist and practitioner, who is interested in smart simulation, fast optimization, learning algorithms, and image processing.

The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics)

Business & Money

Kroese, Dirk P.

Rubinstein, Reuven Y.

Management & Leadership

Springer

This accessible new edition explores the major topics in Monte Carlo simulationSimulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences.The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including:Markov Chain Monte CarloVariance reduction techniques such as the transform likelihood ratio method and the screening methodThe score function method for sensitivity analysisThe stochastic approximation method and the stochastic counter-part method for Monte Carlo optimizationThe cross-entropy method to rare events estimation and combinatorial optimizationApplication of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy methodAn extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB programs.Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.

Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)

Wiley-Interscience

This accessible new edition explores the major topics in Monte Carlo simulation Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: <ul>
<li>Markov Chain Monte Carlo</li> <li>Variance reduction techniques such as the transform likelihood ratio method and the screening method</li> <li>The score function method for sensitivity analysis</li> <li>The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization</li> <li>The cross-entropy method to rare events estimation and combinatorial optimization</li> <li>Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method</li> </ul>
An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.

Simulation and the Monte Carlo Method

Computers & Technology

Computer Science

This set contains the text Simulation and the Monte Carlo Method, Second Edition 9780470177945 and the Student Solutions Manual to Accompany Simulation and the Monte Carlo Method, Second Edition 9780470258798.

Simulation and the Monte Carlo Method, 2nd Edition Set (Wiley Series in Probability and Statistics)

Science & Math

Mathematics

This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.

A step-by-step guide for today's modeling and simulation practices This new guide for modeling and simulation of discrete-event systems (DES) demonstrates why simulation is fast becoming the method of choice for the evaluation of system performance in science, engineering, and management. The book begins with the basics of conventional simulation, then proceeds to modern simulation-treating sensitivity analysis and optimization in a wide range of systems that exhibit complex interaction of discrete events. These include communications networks, flexible manufacturing systems, PERT (project evaluation and review techniques) networks, queueing systems, and more. Less focused on theory than on presenting a clear approach to practical applications, Modern Simulation and Modeling: * Emphasizes concepts rather than mathematical completeness * Integrates references and explanations of complex topics into the body of the text * Provides an innovative chapter on rare-event probability estimation * Describes the implementation of the score function (SF) method using the NSO simulation package * Features 40 illustrations and numerous algorithms * Offers extensive, end-of-chapter exercise sets * Includes chapter bibliographies for further reading Modern Simulation and Modeling is an essential text for graduate students of DES and stochastic processes and for undergraduate students in simulation. It is also an excellent reference for professionals in statistics and probability, mathematics, and management science.

Modern Simulation and Modeling (Wiley Series in Probability and Statistics)

Melamed, Benjamin

A theoretical treatment of Monte Carlo optimization--simulation using perturbation analysis, adaptive methods, and variance reduction techniques. Emphasizes concepts rather than mathematical completeness. Shows how to use simulation and Monte Carlo methods efficiently for estimating performance measures, sensitivities and optimization of stochastic systems.

Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section)

wiley

Uses a conceptual approach to deal with sensitivities of the performance measure with respect to decision variables and offers guidance in improving decisions and identifying the most important ones. Topics covered include sensitivity analysis and optimization of discrete event static and discrete event dynamic systems, a unified framework for the SF method, important sampling, rare events, bottleneck networks and extensions such as autocorrelated input processes.

Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method (Wiley Series in Probability and Mathematical Statistics)

Shapiro, Alexander

A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the field, the book places emphasis on cross-entropy, minimum cross-entropy, splitting, and stochastic enumeration. Focusing on the concepts and application of Monte Carlo techniques, Fast Sequential Monte Carlo Methods for Counting and Optimization includes: <ul>
<li>Detailed algorithms needed to practice solving real-world problems</li> <li>Numerous examples with Monte Carlo method produced solutions within the 1-2% limit of relative error</li> <li>A new generic sequential importance sampling algorithm alongside extensive numerical results</li> <li>An appendix focused on review material to provide additional background information</li> </ul>
Fast Sequential Monte Carlo Methods for Counting and Optimization is an excellent resource for engineers, computer scientists, mathematicians, statisticians, and readers interested in efficient simulation techniques. The book is also useful for upper-undergraduate and graduate-level courses on Monte Carlo methods.

Fast Sequential Monte Carlo Methods for Counting and Optimization (Wiley Series in Probability and Statistics)

Vaisman, Radislav

Ridder, Ad

This accessible new edition explores the major topics in Monte Carlo simulation that have arisen over the past 30 years and presents a sound foundation for problem solving Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as importance (re-)sampling, and the transform likelihood ratio method, the score function method for sensitivity analysis, the stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization, the cross-entropy method for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples. The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search. In addition, the Third Edition features new material on: • Random number generation, including multiple-recursive generators and the Mersenne Twister • Simulation of Gaussian processes, Brownian motion, and diffusion processes • Multilevel Monte Carlo method • New enhancements of the cross-entropy (CE) method, including the “improved” CE method, which uses sampling from the zero-variance distribution to find the optimal importance sampling parameters • Over 100 algorithms in modern pseudo code with flow control • Over 25 new exercises Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. Reuven Y. Rubinstein, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. The author of over 100 articles and six books, Dr. Rubinstein was also the inventor of the popular score-function method in simulation analysis and generic cross-entropy methods for combinatorial optimization and counting. Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics in the School of Mathematics and Physics of The University of Queensland, Australia. He has published over 100 articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic modeling.

See the best price to sell, buy, or rent books by Rubinstein, Reuven Y.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Rubinstein, Reuven Y.

See the best price to sell, buy, or rent books by Rubinstein, Reuven Y.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning (Information Science and Statistics)

Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)

Simulation and the Monte Carlo Method

Simulation and the Monte Carlo Method, 2nd Edition Set (Wiley Series in Probability and Statistics)

Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)

Modern Simulation and Modeling (Wiley Series in Probability and Statistics)

Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks (Wiley Series in Probability and Statistics - Applied Probability and Statistics Section)

Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method (Wiley Series in Probability and Mathematical Statistics)

Fast Sequential Monte Carlo Methods for Counting and Optimization (Wiley Series in Probability and Statistics)

Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)