Bayesian Process Monitoring Control And Optimization
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Bayesian Process Monitoring, Control and Optimization
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 dev
Techniques, Tools and Methodologies Applied to Quality Assurance in Manufacturing
Author: Jorge Luis García Alcaraz
language: en
Publisher: Springer Nature
Release Date: 2021-05-18
This book presents a collection of real cases from industrial practices that production system and quality managers implement to ensure a high quality as well as a low cost in products. This book is divided in sections that are focused on: · The quality and philosophies implemented to production systems; starting from the product design as well as from the supply system. · The principal statistical techniques applied to the quality assurance (statistical quality control, analysis of tests and failure, quality function deployment, accelerated life tests, among others), the process of gathering information, its validation, its reliability process, and techniques for data analysis. · The techniques applied to the integration of human resources in the process of quality assurance, such as managers and operators’ participation, training, and training processes. · Use of information and communications technologies, software, and programs implemented to guarantee the quality of the products in the production systems. ISO standards and policies that are used for quality management and monitoring.
Bayesian Biostatistics
Author: Emmanuel Lesaffre
language: en
Publisher: John Wiley & Sons
Release Date: 2012-08-13
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials. Key Features: Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques. Contains introductory explanations of Bayesian principles common to all areas of application. Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics. Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs. Highlights the differences between the Bayesian and classical approaches. Supported by an accompanying website hosting free software and case study guides. Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful.