Bayesian Process Monitoring, Control and Optimization

This reference presents a state-of-the-art survey of the applications of Bayesian statistics in process monitoring, control, and optimization. Addressing challenges faced by engineers, the book adopts Bayesian approaches for actual industrial practices. It solves these problems through modern computational techniques, such as Markov chain Monte Carlo (MCMC) and other Monte Carlo simulation-based approaches. The book illustrates MCMC with the variance component model, using WinBUGS® and CODA. The authors also explore the advantages and the disadvantages of Bayesian techniques and frequentist approaches. Additional coverage includes inferential problems 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.