Bayesian Methods in Reliability

Bayesian Methods in Reliability edited by P. Sander, R. Badoux.

When data is collected on failure or survival a list of times is obtained. Some of the times are failure times and others are the times at which the subject left the experiment. These times both give information about the performance of the system. The two types will be referred to as failure and ce...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Sander, P. (Editor), Badoux, R. (Editor)
Format: eBook
Language:English
Published: Dordrecht : Springer Netherlands : Imprint: Springer, 1991.
Edition:1st ed. 1991.
Series:Topics in Safety, Reliability and Quality, 1
Springer eBook Collection.
Subjects:
Quality control.
Reliability.
Industrial safety.
Statistics .
Engineering.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 1. Introduction to Bayesian Methods in Reliability
  • 1. Why Bayesian Methods?
  • 2. Bayes’ Theorem
  • 3. Examples from a Safety Study on Gas transmission Pipelines
  • 4. Conclusions
  • References
  • 2. An Overview of the Bayesian Approach
  • 1. Background
  • 2. Probability Concepts
  • 3. Notation
  • 4. Reliability Concepts and Models
  • 5. Forms of Data
  • 6. Statistical Problems
  • 7. Review of Non-Bayesian Statistical Methods
  • 8. Desiderata for Decision-Oriented Statistical Methodology
  • 9. Decision-Making
  • 10. Degrees of Belief as Probabilities
  • 11. Bayesian Statistical Philosophy
  • 12. A Simple Illustration of Bayesian Learning
  • 13. Bayesian Approaches to Typical Statistical Questions
  • 14. Assessment of Prior Densities
  • 15. Bayesian Inference for some Univariate Probability Models
  • 16. Approximate Analysis under Great Prior Uncertainty
  • 17. Problems Involving many Parameters: Empirical Bayes
  • 18. Numerical Methods for Practical Bayesian Statistics
  • References
  • 3. Reliability Modelling and Estimation
  • 1. Non-Repairable Systems
  • 2. Estimation
  • 3. Reliability estimation
  • References
  • 4. Repairable Systems and Growth Models
  • 1. Introduction
  • 2. Good as New: the Renewal Process
  • 3. Estimation
  • 4. The Poisson Process
  • 5. Bad as old: the Non-Homogeneous Poisson Process
  • 6. Classical Estimation
  • 7. Exploratory Analysis
  • 8. The Duane Model
  • 9. Bayesian Analysis
  • References
  • 5. The Use of Expert Judgement in Risk Assessment
  • 1. Introduction
  • 2. Independence Preservation
  • 3. The Quality of Experts’ Judgement
  • 4. Calibration Sets and Seed Variables
  • 5. A Classical Model
  • 6. Bayesian Models
  • 7. Some Experimental Results
  • References
  • 6. Forecasting Software Reliability
  • 1. Introduction
  • 2. The Software Reliability Growth Problem
  • 3. Some Software Reliability Growth Models
  • 4. Examples of Use
  • 5. Analysis of Predictive Quality
  • 6. Examples of Predictive Analysis
  • 7. Adapting and Combining Predictions; Future Directions
  • 8 Summary and Conclusions
  • Acknowledgements
  • References
  • References
  • Author index.

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