This is an ideal textbook for students of experimental optimization techniques used in industrial production processes. It presents a detailed treatment of Bayesian Optimization approaches and it contains a mix of technical and practical sections.

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.

A much stronger treatment of the topic than the Wiley books published in this area for these reasons: (1) on the strength of the book's author and (2) on its coverage and treatment of process optimization Provides in the form of a text a contemporary account not only of the classical techniques and tools used in Design of Experiments (DOE) and Response Surface Methods (RSM), but also to present more advanced process optimization techniques from the recent literature which has not been used that much in industrial practice 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 Includes supplementary material: sn.pub/extras

Inhalt
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.