Enrique Del Castillo

Process Optimization (eBook, PDF)

A Statistical Approach

• Format: PDF

Produktbeschreibung

This book is an ideal textbook for a second course in experimental optimization techniques for industrial production processes. In addition, it is a superb reference volume for use by professors and graduate students in Industrial Engineering and Statistics departments. It will also be of huge interest to applied statisticians, process engineers, and quality engineers working in the electronics and biotech manufacturing industries. In all, it provides an in-depth presentation of the statistical issues that arise in optimization problems, including, amongst other things, confidence regions on the optimal settings of a process and stopping rules in experimental optimization. It presents a detailed treatment of Bayesian Optimization approaches. It contains a mix of technical and practical sections, appropriate for a first year graduate text in the subject or useful for self-study or reference.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 462
• Erscheinungstermin: 14. September 2007
• Englisch
• ISBN-13: 9780387714356
• Artikelnr.: 43728001

Autorenporträt

Enrique del Castillo, The Pennsylvania State University, PA, USA

Inhaltsangabe

Preliminaries.- An Overview of Empirical Process Optimization.- Elements of Response Surface Methods.- Optimization Of First Order Models.- Experimental Designs For First Order Models.- Analysis and Optimization of Second Order Models.- Experimental Designs for Second Order Models.- Statistical Inference in Process Optimization.- Statistical Inference in First Order RSM Optimization.- Statistical Inference in Second Order RSM Optimization.- Bias Vs. Variance.- Robust Parameter Design and Robust Optimization.- Robust Parameter Design.- Robust Optimization.- Bayesian Approaches in Process Optimization.- to Bayesian Inference.- Bayesian Methods for Process Optimization.- to Optimization of Simulation and Computer Models.- Simulation Optimization.- Kriging and Computer Experiments.- Appendices.- Basics of Linear Regression.- Analysis of Variance.- Matrix Algebra and Optimization Results.- Some Probability Results Used in Bayesian Inference.