Bayesian Statistics: A Review and a Reminder for the Practicing Reliability Engineer

TL;DR

This paper reviews Bayesian reliability methods, emphasizing success/no-success data analysis and a Monte Carlo algorithm for calculating system reliability posteriors, aiding reliability engineers with limited component data.

Contribution

It provides a clear overview of Bayesian reliability principles and introduces a practical Monte Carlo algorithm for system reliability estimation from system-level data.

Findings

Effective Bayesian methods for success/no-success data analysis

A simple Monte Carlo algorithm for posterior reliability calculation

Useful for systems with component reliability data limitations

Abstract

This paper introduces and reviews some of the principles and methods used in Bayesian reliability. It specifically discusses methods used in the analysis of success/no-success data and then reminds the reader of a simple Monte Carlo algorithm that can be used to calculate the posterior distribution of a system's reliability. This algorithm is especially useful when a system's reliability is modeled through the reliability of its subcomponents, yet only system-level data is available.