Essentials of Monte Carlo Simulation

Statistical Methods for Building Simulation Models

Nonfiction, Science & Nature, Mathematics, Statistics, Computers, Application Software
Cover of the book Essentials of Monte Carlo Simulation by Nick T. Thomopoulos, Springer New York
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Author: Nick T. Thomopoulos ISBN: 9781461460220
Publisher: Springer New York Publication: December 19, 2012
Imprint: Springer Language: English
Author: Nick T. Thomopoulos
ISBN: 9781461460220
Publisher: Springer New York
Publication: December 19, 2012
Imprint: Springer
Language: English

**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. 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

**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. 

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