With a brief introduction that emphasizes inferential problems, this book presents a state-of-the-art survey of the applications of Bayesian statistics in process monitoring, control, and optimization. It addresses the many problems faced by engineers involved in monitoring, controlling, and optimizing industrial processes. It focuses on facing these challenges through modern computational techniques such as Markov Chain Monte Carlo and other Monte Carlo simulation-based approaches. It also presents modern numerical methods for Bayesian inference including the use of WinBUGS. The authors explore the advantages and the disadvantages of both Bayesian techniques and frequentist approaches.
PART I: INTRODUCTION TO BAYESIAN INFERENCE
An Introduction to Bayesian Inference in Process Monitoring, Control, and Optimization
Enrique del Castillo and Bianca M. Colosimo
Modern Numerical Methods in Bayesian Computation
Bianca M. Colosimo and Enrique del Castillo
PART II: PROCESS MONITORING
A Bayesian Approach to Statistical Process Control
Panagiotis Tsiamyrtzis and Douglas M. Hawkins
Empirical Bayes Process Monitoring Techniques
Jyh-Jen H. Shiau and Carol J. Feltz
A Bayesian Approach to Monitoring the Mean of a Multivariate Normal Process
Frank B. Alt
Two-Sided Bayesian Control Charts for Short Production Runs
George Tagaras and George Nenes
Bayes' Rule of Information and Monitoring in Manufacturing Integrated Circuits
Spencer Graves
PART III: PROCESS CONTROL AND TIME SERIES ANALYSIS
A Bayesian Approach to Signal Analysis of Pulse Trains
Melinda Hock and Refik Soyer
Bayesian Approaches to Process Monitoring and Process Adjustment
Rong Pan
PART IV: PROCESS OPTIMIZATION AND DESIGNED EXPERIMENTS
A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization
John J. Peterson
An Application of Bayesian Statistics to Sequential Empirical Optimization
Carlos W. Moreno
Bayesian Estimation from Saturated Factorial Designs
Marta Y. Baba and Steven G. Gilmour
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