Monte Carlo: Concepts, Algorithms, and Applications
This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and advanced undergraduate students in the mathematical and engineering sciences. It also can serve as a reference for the professional analyst. In the past, my inability to provide students with a single­ source book on this topic for class and for later professional reference had left me repeatedly frustrated, and eventually motivated me to write this book. In addition to focused accounts of major topics, the book has two unifying themes: One concerns the effective use of information and the other concerns error control and reduction. The book describes how to incorporate information about a problem into a sampling plan in a way that reduces the cost of estimating its solution to within a specified error bound. Although exploiting special structures to reduce cost long has been a hallmark of the Monte Carlo method, the propen­ sity of users of the method to discard useful information because it does not fit traditional textbook models repeatedly has impressed me. The present account aims at reducing the impediments to integrating this information. Errors, both statistical and computational, abound in every Monte Carlo sam­ pling experiment, and a considerable methodology exists for controlling them.
1117164188
Monte Carlo: Concepts, Algorithms, and Applications
This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and advanced undergraduate students in the mathematical and engineering sciences. It also can serve as a reference for the professional analyst. In the past, my inability to provide students with a single­ source book on this topic for class and for later professional reference had left me repeatedly frustrated, and eventually motivated me to write this book. In addition to focused accounts of major topics, the book has two unifying themes: One concerns the effective use of information and the other concerns error control and reduction. The book describes how to incorporate information about a problem into a sampling plan in a way that reduces the cost of estimating its solution to within a specified error bound. Although exploiting special structures to reduce cost long has been a hallmark of the Monte Carlo method, the propen­ sity of users of the method to discard useful information because it does not fit traditional textbook models repeatedly has impressed me. The present account aims at reducing the impediments to integrating this information. Errors, both statistical and computational, abound in every Monte Carlo sam­ pling experiment, and a considerable methodology exists for controlling them.
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Monte Carlo: Concepts, Algorithms, and Applications

Monte Carlo: Concepts, Algorithms, and Applications

by George Fishman
Monte Carlo: Concepts, Algorithms, and Applications

Monte Carlo: Concepts, Algorithms, and Applications

by George Fishman

Overview

This book provides an introduction to the Monte Carlo method suitable for a one-or two-semester course for graduate and advanced undergraduate students in the mathematical and engineering sciences. It also can serve as a reference for the professional analyst. In the past, my inability to provide students with a single­ source book on this topic for class and for later professional reference had left me repeatedly frustrated, and eventually motivated me to write this book. In addition to focused accounts of major topics, the book has two unifying themes: One concerns the effective use of information and the other concerns error control and reduction. The book describes how to incorporate information about a problem into a sampling plan in a way that reduces the cost of estimating its solution to within a specified error bound. Although exploiting special structures to reduce cost long has been a hallmark of the Monte Carlo method, the propen­ sity of users of the method to discard useful information because it does not fit traditional textbook models repeatedly has impressed me. The present account aims at reducing the impediments to integrating this information. Errors, both statistical and computational, abound in every Monte Carlo sam­ pling experiment, and a considerable methodology exists for controlling them.

Product Details

ISBN-13: 9781441928474
Publisher: Springer New York
Publication date: 05/26/2011
Series: Springer Series in Operations Research and Financial Engineering
Edition description: Softcover reprint of hardcover 1st ed. 1996
Pages: 698
Product dimensions: 7.01(w) x 10.00(h) x 0.06(d)

Table of Contents

1 Introduction.- 2 Estimating Volume and Count.- 3 Generating Samples.- 4 Increasing Efficiency.- 5 Random Tours.- 7 Generating Pseudorandom Numbers.- Author Index.
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