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This book covers several bases at once. It is useful as a
textbook for a second course in experimental optimization
techniques for industrial production processes. In addition, it is
a superb reference volume for use by professors and graduate
students in Industrial Engineering and Statistics departments. It
will also be of huge interest to applied statisticians, process
engineers, and quality engineers working in the electronics and
biotech manufacturing industries. In all, it provides an in-depth
presentation of the statistical issues that arise in optimization
problems, including confidence regions on the optimal settings of a
process, stopping rules in experimental optimization, and more.
This book covers several bases at once. It is useful as a
textbook for a second course in experimental optimization
techniques for industrial production processes. In addition, it is
a superb reference volume for use by professors and graduate
students in Industrial Engineering and Statistics departments. It
will also be of huge interest to applied statisticians, process
engineers, and quality engineers working in the electronics and
biotech manufacturing industries. In all, it provides an in-depth
presentation of the statistical issues that arise in optimization
problems, including confidence regions on the optimal settings of a
process, stopping rules in experimental optimization, and more.
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.
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