Process Optimization: A Statistical ApproachPROCESS OPTIMIZATION: A Statistical Approach is a textbook for a course in experimental optimization techniques for industrial production processes and other "noisy" systems where the main emphasis is process optimization. The book can also be used as a reference text by Industrial, Quality and Process Engineers and Applied Statisticians working in industry, in particular, in semiconductor/electronics manufacturing and in biotech manufacturing industries. The major features of PROCESS OPTIMIZATION: A Statistical Approach are:
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Contents
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OPTIMIZATIONOFFIRSTORDERMODELS | 29 |
EXPERIMENTALDESIGNSFORFIRSTORDERMODELS | 45 |
ANALYSIS AND OPTIMIZATION OF SECOND ORDER | 85 |
EXPERIMENTAL DESIGNS FOR SECOND ORDER | 109 |
STATISTICAL INFERENCE IN FIRST ORDER | 159 |
STATISTICAL INFERENCE IN SECOND ORDER | 193 |
BIAS VS VARIANCE | 209 |
INTRODUCTION TO BAYESIAN INFERENCE | 291 |
BAYESIAN METHODS FOR PROCESS OPTIMIZATION | 321 |
SIMULATION OPTIMIZATION | 367 |
KRIGING AND COMPUTER EXPERIMENTS | 379 |
Appendices | 399 |
B Analysis of Variance | 413 |
Matrix Algebra and Optimization Results | 429 |
Some Probability Results used in Bayesian Inference | 443 |
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Common terms and phrases
analysis applied approach assumed Bayesian block called Chapter coded columns compared compute Consider constraints contains controllable factors corresponding defined denotes density desired determine direction discussed distribution effects elements Engineering equal equation error estimates example expected experiment experimental design Figure fraction function given gives idea implies important interactions interest levels linear matrix maximum mean measure method minimize noise factors normal Note observed obtained operating optimization order model origin orthogonal parameter parameter estimates performance plot possible posterior practice predictive prior probability problem properties proposed provides quadratic random region relation respect response result robust rotatable rule sampling scaled second order shown shows simulation solution squares statistical steepest ascent step stopping Suppose Table techniques true variables variance vary vector zero