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Author: Farouk Mollah Banna Publisher: ISBN: Category : Monte Carlo method Languages : en Pages : 172
Book Description
Compares Monte Carlo simulation, where inputs are represented by probability distribution functions, and the stochastic response surface method (SRSM), where inputs and outputs are approximated using series expansions of standard random variables, in water quality modeling using Streeter Phelps equations. Uses the specific case of water quality problems of the Neuse River in North Carolina to compare these two methods. Seeks to determine how the modeling results representation can be better improved by developing and analyzing their output probability distributions based on input variability, the optimal number of runs for the two methods, and how the two methods compare in terms of results efficiency and computational effort.
Author: Farouk Mollah Banna Publisher: ISBN: Category : Monte Carlo method Languages : en Pages : 172
Book Description
Compares Monte Carlo simulation, where inputs are represented by probability distribution functions, and the stochastic response surface method (SRSM), where inputs and outputs are approximated using series expansions of standard random variables, in water quality modeling using Streeter Phelps equations. Uses the specific case of water quality problems of the Neuse River in North Carolina to compare these two methods. Seeks to determine how the modeling results representation can be better improved by developing and analyzing their output probability distributions based on input variability, the optimal number of runs for the two methods, and how the two methods compare in terms of results efficiency and computational effort.
Author: Reuven Y. Rubinstein Publisher: John Wiley & Sons ISBN: 1118632389 Category : Mathematics Languages : en Pages : 470
Book Description
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.
Author: Arnaud Doucet Publisher: Springer Science & Business Media ISBN: 1475734379 Category : Mathematics Languages : en Pages : 590
Book Description
Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.
Author: Dirk P. Kroese Publisher: John Wiley & Sons ISBN: 0470285303 Category : Mathematics Languages : en Pages : 204
Book Description
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: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening 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 to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method 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.
Author: Farouk Mollah Banna
Author: Farouk Mollah Banna
Author: Sjors van de Kamer
Author: Ronald F. Malone
Author: Leslie Franklin Grober
Author: Mark A. Tumeo
Author: Ding-Geng (Din) Chen
Author: Reuven Y. Rubinstein
Author: 
Author: Arnaud Doucet
Author: Dirk P. Kroese