## Bayesian process monitoring, control and optimizationAlthough 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. |

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### Contents

Control and Optimization | 12 |

Modern Numerical Methods in Bayesian Computation | 47 |

A Bayesian Approach to Statistical Process Control | 87 |

Copyright | |

10 other sections not shown

### Other editions - View all

Bayesian Process Monitoring, Control and Optimization Bianca M. Colosimo,Enrique del Castillo Limited preview - 2006 |

Bayesian Process Monitoring, Control and Optimization Bianca M. Colosimo,Enrique del Castillo No preview available - 2006 |

### Common terms and phrases

algorithm applications approximation assume average Bayesian analysis Bayesian approach Bayesian inference Bayesian procedure Bayesian statistics burn-in Castillo chapter components compute conjugate prior control charts convergence covariance matrix CUSUM denote detecting discretization empirical Bayes Equation error estimate EWMA controller example exponential factors Figure follows frequentist Gibbs sampler given hyperparameters IMBP in-control ARLs inputs integral iteration joint posterior Journal of Quality Kalman filter marginal likelihood Markov chain Markov Chain Monte MCMC MEWMAE procedure missing pulses mixture monitoring scheme multivariate normal non-informative prior normal distribution observation obtained optimal out-of-control performance posterior density posterior distribution posterior mean posterior probability Prior and Posterior prior distribution problem process control process mean vector process monitoring quadratic quality control random variable recursive regression model response surface RMBP run length saturated designs score Section sequential setup Shewhart shifts simulation Table theorem tion univariate updating values variance variance-covariance matrix weights WinBUGS X-chart