Presents a state-of-the-art survey of the engineering applications of Bayesian statistics in process monitoring, control, and optimization

Emphasizes modern computational techniques, such as Markov chain Monte Carlo (MCMC) and other simulation approaches

Explores the advantages and disadvantages of Bayesian techniques and frequentist approaches

Illustrates MCMC with the variance component model, using WinBUGS® and CODA

Demonstrates how Bayesian methods can be successfully applied in SPC, process adjustment, experimental design, and response surface methods (RSM)

Although there are many Bayesian statistical books that focus on biostatistics and economics, there are few that address the problems faced by engineers. Bayesian Process Monitoring, Control and Optimization resolves this need, showing you how to oversee, adjust, and optimize industrial processes.

Bridging the gap between application and development, this reference adopts Bayesian approaches for actual industrial practices. Divided into four parts, it begins with an introduction that discusses inferential problems and presents modern methods in Bayesian computation. The next part explains statistical process control (SPC) and examines both univariate and multivariate process monitoring techniques. Subsequent chapters present Bayesian approaches that can be used for time series data analysis and process control. The contributors include material on the Kalman filter, radar detection, and discrete part manufacturing. The last part focuses on process optimization and illustrates the application of Bayesian regression to sequential optimization, the use of Bayesian techniques for the analysis of saturated designs, and the function of predictive distributions for optimization.

Written by international contributors from academia and industry, Bayesian Process Monitoring, Control and Optimization provides up-to-date applications of Bayesian processes for industrial, mechanical, electrical, and quality engineers as well as applied statisticians.

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

PROCESS MONITORING
A Bayesian Approach to Statistical Process Control
Panagiotis Tsiamyrtzis and Douglas M. Hawkins
Empirical Bayes Process Monitoring Techniques
Jyh-Jen Horng 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

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

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

Index

Editorial Reviews

"Overall, this is a nice reference text . . . The editors have done a nice job keeping the notation consistent throughout, and the book is well organized. An invaluable component of each chapter is the accompanying extensive list of references . . ."

– Timothy J. Robinson, University of Wyoming, in JASA, May 2008, Vol. 62, No. 6

There are no downloads nor updates currently available for this title.