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Simulation is a widely used methodology in all the Applied Science disciplines. It provides a flexible, powerful and intuitive tool for investigating how to design a process or system, and how to maximize its efficiency. Simulation's effectiveness depends greatly on how well the simulation experiments are designed and analyzed. Design and Analysis of Simulation Experiments will focus on this crucial phase in the overall process of applying simulation. The book will include the best of both classic and modern methods of simulation experimentation, and as a result, it will provide a…mehr
Simulation is a widely used methodology in all the Applied Science disciplines. It provides a flexible, powerful and intuitive tool for investigating how to design a process or system, and how to maximize its efficiency. Simulation's effectiveness depends greatly on how well the simulation experiments are designed and analyzed. Design and Analysis of Simulation Experiments will focus on this crucial phase in the overall process of applying simulation. The book will include the best of both classic and modern methods of simulation experimentation, and as a result, it will provide a state-of-the-art treatment of the topic. This book will be the standard reference book on the topic for both researchers and sophisticated practitioners in the area, and it will be used as a textbook in courses or seminars focusing on this topic.
Jack Kleijnen is well known internationally for being a leading researcher in simulation for more than 30 years. He is the author of highly cited books in the area of statistical techniques in simulation that were published between 1974 and 1992. He is an excellent writer and researcher, and hence, ideally suited to write this important book for the field.
On 25 February 2008 Her Majesty Beatrix, Queen of the Netherlands, appointed Jack Kleijnen a Knight in the Order of the Netherlands Lion.
Inhaltsangabe
Preface.- Introduction.- Black Box Metamodels.- Low-Order Polynomial Regression.- Metamodels and Designs: A Single Factor.- Low-Order Polynomial Models and Designs: Multiple Factors.- Low-Order Polynomial Models and Screening Designs: Hundreds of Factors.- Kriging Metamodels.- Latin Hypercube Sampling (LHS) and other Space-Filling Designs.- Cross-Validation of Metamodels.- Conclusions and Further Research.
Preface.- Introduction.- Black Box Metamodels.- Low-Order Polynomial Regression.- Metamodels and Designs: A Single Factor.- Low-Order Polynomial Models and Designs: Multiple Factors.- Low-Order Polynomial Models and Screening Designs: Hundreds of Factors.- Kriging Metamodels.- Latin Hypercube Sampling (LHS) and other Space-Filling Designs.- Cross-Validation of Metamodels.- Conclusions and Further Research.
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