Bayesian Process Monitoring, Control and Optimization
Bianca M. Colosimo, Enrique Del Castillo
Résumé
- 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.
L'auteur - Bianca M. Colosimo
Bianca M. Colosimo : Politecnico di Milano, Italy
L'auteur - Enrique Del Castillo
Enrique Del Castillo : Pennsylvania State University, University Park, USA
Sommaire
- Introduction to bayesian inference
- An Introduction to Bayesian Inference in Process Monitoring, Control, and Optimization
- Modern Numerical Methods in Bayesian Computation
- Process Monitoring
- A Bayesian Approach to Statistical Process Control
- Empirical Bayes Process Monitoring Techniques
- A Bayesian Approach to Monitoring the Mean of a Multivariate Normal Process
- Two-Sided Bayesian Control Charts for Short Production Runs
- Bayes' Rule of Information and Monitoring in Manufacturing Integrated Circuits
- Process Control And Time Series Analysis
- A Bayesian Approach to Signal Analysis of Pulse Trains
- Bayesian Approaches to Process Monitoring and Process Adjustment
- Process Optimization And Designed Experiments
- A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization
- An Application of Bayesian Statistics to Sequential Empirical Optimization
- Bayesian Estimation from Saturated Factorial Designs
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Chapman and Hall / CRC |
| Auteur(s) | Bianca M. Colosimo, Enrique Del Castillo |
| Parution | 20/11/2006 |
| Nb. de pages | 320 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 620g |
| Intérieur | Noir et Blanc |
| EAN13 | 9781584885443 |
| ISBN13 | 978-1-58488-544-3 |
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