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).