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E-raamat: Bayesian Process Monitoring, Control and Optimization [Taylor & Francis e-raamat]

  • Formaat: 352 pages
  • Ilmumisaeg: 19-Sep-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429140778
  • Taylor & Francis e-raamat
  • Hind: 180,03 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 257,19 €
  • Säästad 30%
  • Formaat: 352 pages
  • Ilmumisaeg: 19-Sep-2019
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429140778
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.
Part I Introduction to Bayesian Inference
1 An Introduction to Bayesian Inference in Process Monitoring, Control and Optimization
3(44)
Enrique del Castillo
Bianca M. Colosimo
2 Modern Numerical Methods in Bayesian Computation
47(40)
Bianca M. Colosimo
Enrique del Castillo
Part II Process Monitoring
3 A Bayesian Approach to Statistical Process Control
87(22)
Panagiotis Tsiamyrtzis
Douglas M. Hawkins
4 Empirical Bayes Process Monitoring Techniques
109(30)
Jyh-Jen Horng Shiau
Carol J. Feltz
5 A Bayesian Approach to Monitoring the Mean of a Multivariate Normal Process
139(28)
Frank B. Alt
6 Two-Sided Bayesian X Control Charts for Short Production Runs
167(20)
George Tagaras
George Nenes
7 Bayes' Rule of Information and Monitoring in Manufacturing Integrated Circuits
187(28)
Spencer Graves
Part III Process Control and Time Series Analysis
8 A Bayesian Approach to Signal Analysis of Pulse Trains
215(30)
Melinda Hock
Refik Soyer
9 Bayesian Approaches to Process Monitoring and Process Adjustment
245(24)
Rong Pan
Part IV Process Optimization and Designed Experiments
10 A Review of Bayesian Reliability Approaches to Multiple Response Surface Optimization
269(22)
John J. Peterson
11 An Application of Bayesian Statistics to Sequential Empirical Optimization
291(20)
Carlos W. Moreno
12 Bayesian Estimation from Saturated Factorial Designs
311(22)
Marta Y. Baba
Steven G. Gilmour
Index 333
Bianca M. Colosimo, Enrique del Castillo