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Zusatztext ? this volume is a special collection of informative and valuable articles in industrial statistics! particularly in the areas of process monitoring! control/adjustment! and optimization. The volume includes contributors from different parts of the world in both academia and industry sharing their research knowledge! experience! and wisdom in this particular area. In addition! this volume demonstrates the great effort being made to reach out to researchers in this area from both industry and academia.-Technometrics! May 2009! Vol. 51! No. 2Overall! 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! JASA! May 2008! Vol. 62! No. 6 Informationen zum Autor Bianca M. Colosimo! Enrique del Castillo Klappentext 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). Zusammenfassung Presents an introduction that discusses inferential problems and presents modern methods in Bayesian computation. This work explains statistical process control (SPC) and examines both univariate and multivariate process monitoring techniques. It includes material on the Kalman filter, radar detection and discrete part manufacturing. Inhaltsverzeichnis Introduction to Bayesian Inference. Process Monitoring. Process Control and Time Series Analysis. Process Optimization and Designed Experiments. ...
