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Design and Modeling for Computer Experiments [Chapman & Hall/CRC Computer Scienc

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Características del artículo

Estado
En buen estado: Libro que se ha leído pero que está en buen estado. Daños mínimos en la tapa, ...
ISBN
9781584885467

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Product Identifiers

Publisher
CRC Press LLC
ISBN-10
1584885467
ISBN-13
9781584885467
eBay Product ID (ePID)
47778029

Product Key Features

Number of Pages
304 Pages
Publication Name
Design and Modeling for Computer Experiments
Language
English
Publication Year
2005
Subject
Computer Simulation, Probability & Statistics / General, General, Research & Methodology
Type
Textbook
Author
Runze Li, Kai-Tai Fang, Agus Sudjianto
Subject Area
Mathematics, Computers, Science
Series
Chapman and Hall/Crc Computer Science and Data Analysis Ser.
Format
Hardcover

Dimensions

Item Height
0.8 in
Item Weight
20 Oz
Item Length
9.3 in
Item Width
6.4 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2005-051751
Dewey Edition
22
Series Volume Number
Vol. 6
Illustrated
Yes
Dewey Decimal
003/.3
Table Of Content
PART I AN OVERVIEW INTRODUCTION Experiments and Their Statistical Designs Some Concepts in Experimental Design Computer Experiments Examples of Computer Experiments Space-Filling Designs Modeling Techniques Sensitivity Analysis Strategies for Computer Experiments and An Illustration Case Study Remarks on Computer Experiments Guidance of Reading This Book PART II DESIGNS FOR COMPUTER EXPERIMENTS Latin Hypercube Sampling and its Modifications Uniform Experimental Design Optimization in Construction of Designs for Computer Experiments PART III MODELING FOR COMPUTER EXPERIMENTS METAMODELING Model Interpretation Functional Response APPENDIX Abbreviation References Index Author Index
Synopsis
Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific theoretical/mathematical examinations of computer experiment design are available, those interested in applying proposed methodologies need a practical presentation and straightforward guidance on analyzing and interpreting experiment results. Written by authors with strong academic reputations and real-world practical experience, Design and Modeling for Computer Experiments is exactly the kind of treatment you need. The authors blend a sound, modern statistical approach with extensive engineering applications and clearly delineate the steps required to successfully model a problem and provide an analysis that will help find the solution. Part I introduces the design and modeling of computer experiments and the basic concepts used throughout the book. Part II focuses on the design of computer experiments. The authors present the most popular space-filling designs - like Latin hypercube sampling and its modifications and uniform design - including their definitions, properties, construction and related generating algorithms. Part III discusses the modeling of data from computer experiments. Here the authors present various modeling techniques and discuss model interpretation, including sensitivity analysis. An appendix reviews the statistics and mathematics concepts needed, and numerous examples clarify the techniques and their implementation. The complexity of real physical systems means that there is usually no simple analytic formula that sufficiently describes the phenomena. Useful both as a textbook and professional reference, this book presents the techniques you need to design and model computer experiments for practical problem solving., This book blends a modern statistical approach with extensive engineering applications and clearly delineates the steps for successfully modeling a problem and analyzing it to find the solution. It introduces basic concepts, then fully examines computer experiment design. The authors present the popular space-filling designs - like Latin hypercube sampling - including their properties, construction, and generating algorithms. Discussion then moves to the modeling of data from computer experiments. Here the authors present various modeling techniques and discuss model interpretation, including sensitivity analysis. Numerous examples clarify the techniques and their implementation.
LC Classification Number
QA76.9.C65F36 2005

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