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Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run several times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. Over 100 numerical examples are presented as part of the appendix to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. Very little has been published in the area of computer Monte Carlo simulation methods, and this book will appeal to students and researchers in the fields of Mathematics and Statistics.
List of contents
Introduction.- Random Number Generators.- Generating Random Variates.- Generating Continuous Random Variates.- Generating Discrete Random Variates.- Generating Multivariate Random Variates.- Special Applications.- Output From Simulation Runs.- Analysis Of Output Data.- Choosing the Probability Distribution From Data.- Choosing the Probability Distribution When No Data.- Appendix.- Problems.- Solutions.
About the author
Nick T. Thomopoulos is a professor emeritus at the Illinois Institute of Technology. He is the author of four books and has over 100 published papers and presentations to his credit, and for many years, he has consulted in a wide variety of industries in the United States, Europe, and Asia. He has been the recipient of numerous honors, such as the Rist Prize in 1972 from the Military Operations Research Society for new developments in queuing theory, the Distinguished Professor Award in Bangkok, Thailand in 2005 from the IIT Asian Alumni Association, and the Professional Achievement Award in 2009 from the IIT Alumni Association.
Summary
Essentials of Monte Carlo Simulation focuses on the fundamentals of Monte Carlo methods using basic computer simulation techniques. The theories presented in this text deal with systems that are too complex to solve analytically. As a result, readers are given a system of interest and constructs using computer code, as well as algorithmic models to emulate how the system works internally. After the models are run several times, in a random sample way, the data for each output variable(s) of interest is analyzed by ordinary statistical methods. This book features 11 comprehensive chapters, and discusses such key topics as random number generators, multivariate random variates, and continuous random variates. Over 100 numerical examples are presented as part of the appendix to illustrate useful real world applications. The text also contains an easy to read presentation with minimal use of difficult mathematical concepts. Very little has been published in the area of computer Monte Carlo simulation methods, and this book will appeal to students and researchers in the fields of Mathematics and Statistics.
Additional text
...the author notes that the text is intended for users who want to know more about how the Monte Carlo model “does what it does.” He goes on to further describe the book as telling the user how to cope with simulation models that are associated with two or more variables that are correlated and jointly related, that is, are multivariate in nature. The book will also help users who are confronted with a probability distribution that does not comply with those available in the software they are using...I like the fact that the author uses several examples that are fairly easy to follow for the analyst. Problems assigned to each chapter with corresponding solutions are presented in the appendices for the reader to test their knowledge of the material.
Technometrics 56:1 2014
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...the author notes that the text is intended for users who want to know more about how the Monte Carlo model "does what it does." He goes on to further describe the book as telling the user how to cope with simulation models that are associated with two or more variables that are correlated and jointly related, that is, are multivariate in nature. The book will also help users who are confronted with a probability distribution that does not comply with those available in the software they are using...I like the fact that the author uses several examples that are fairly easy to follow for the analyst. Problems assigned to each chapter with corresponding solutions are presented in the appendices for the reader to test their knowledge of the material.
Technometrics 56:1 2014
