Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

[1]  Oliver Sträter,et al.  Considerations on the elements of quantifying human reliability , 2004, Reliab. Eng. Syst. Saf..

[2]  Robert L. Winkler,et al.  An Introduction to Bayesian Inference and Decision , 1972 .

[3]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[4]  Ali Mosleh Common cause failures: An analysis methodology and examples , 1991 .

[5]  P. C. Sander,et al.  Repairable systems reliability: Modeling, inference, misconceptions and their causes , 1986 .

[6]  Paul L. Meyer,et al.  Introductory Probability and Statistical Applications , 1970 .

[7]  Daniel Gianola,et al.  An Introduction to Bayesian Inference , 2002 .

[8]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[9]  L. Breuer Introduction to Stochastic Processes , 2022, Statistical Methods for Climate Scientists.

[10]  R. Dodge,et al.  PROBLEMS OF HUMAN VARIABILITY. , 1924, Science.

[11]  L. J. Bain,et al.  Introduction to Probability and Mathematical Statistics , 1987 .

[12]  G. D'Agostini Probability and Measurement Uncertainty in Physics - a Bayesian Primer , 1995 .

[13]  Edward B. Fowlkes,et al.  Risk analysis of the space shuttle: Pre-Challenger prediction of failure , 1989 .

[14]  Irem Y. Tumer,et al.  REQUIREMENTS FOR A FAILURE MODE TAXONOMY FOR USE IN CONCEPTUAL DESIGN , 2003 .

[15]  Robert V. Hogg,et al.  Introduction to Mathematical Statistics. , 1966 .

[16]  Piping Division. Design,et al.  Failure data and failure analysis in power and processing industries : presented at the Energy Technology Conference, Houston, Texas, September 18-23, 1977 , 1977 .

[17]  Seth D. Guikema,et al.  A comparison of reliability estimation methods for binary systems , 2005, Reliab. Eng. Syst. Saf..

[18]  P. Laplace A Philosophical Essay On Probabilities , 1902 .

[19]  W. R. Buckland,et al.  A Guide to Probability Theory and Application , 1974 .

[20]  A. B. Hill The Environment and Disease: Association or Causation? , 1965, Proceedings of the Royal Society of Medicine.

[21]  L. Joseph,et al.  Bayesian Statistics: An Introduction , 1989 .

[22]  Hiromitsu Kumamoto,et al.  Designing for reliability and safety control , 1985 .

[23]  Joseph L. Fleiss,et al.  An Introduction to Applied Probability , 2004 .

[24]  Mervi Eerola Probabilistic Causality in Longitudinal Studies , 1994 .

[25]  H. Martz Bayesian reliability analysis , 1982 .

[26]  M. Tribus,et al.  Probability theory: the logic of science , 2003 .