Approximating Posterior Distributions of System Reliability Derived from Component Test Data.

Abstract

The determination of exact interval estimates for system reliability, when only the components of the system have been tested, is difficult both from a theoretical and a computational point of view. This work develops approximations to exact Bayesian intervals using asymptotic expansions and other techniques. Several approximations prove to be very accurate, especially in the region where computation of exact results becomes formidable. The assessment of accuracy is by comparison with exact results, when these are available. For systems with complex structure comparisons are with results obtained by simulating the posterior distribution of system reliability using Monte Carlo methods. Test data considered can be either pass/fail or data obtained from procedures for which failure times are exponentially distributed. Mixed test data and repeated components are also considered. (Author).

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1984
Accession Number
ADA152057

Entities

People

  • A. Winterbottom

Organizations

  • City, University of London

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Asymptotic Series
  • Computations
  • Data Science
  • Information Science
  • Intervals
  • Mathematical Analysis
  • Mathematics
  • Monte Carlo Method
  • Reliability

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms