Beschreibung
InhaltsangabePreface.- An overview of empirical process optimization.- Optimization based on 1st order polynomial models.- Experimental designs for 1st order models.- Analysis and optimization of 2nd order models.- Designs for 2nd order models.- Statistical inference in 1st order RSM.- Statistical inference in 2nd order RSM.- The bias vs. variance debate.- Robust parameter design.- Robust optimization.- Introduction to Bayesian inference.- Bayesian methods process optimization.- Simulation optimization.- Kriging and computer experiments.- Appendices.- References.- Index.
Autorenporträt
InhaltsangabePreliminaries.- 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.
