Specifying Prior Distributions in Reliability Applications

TL;DR

This paper reviews Bayesian methods for reliability data analysis, emphasizing the importance of prior distributions, and discusses extensions to handle practical issues like censoring, aiming for reliable inference with limited data.

Contribution

It evaluates and extends default prior distributions for Bayesian reliability analysis, addressing practical challenges such as censoring in real-world data.

Findings

Bayesian methods provide consistent interval estimates with limited data.

Default priors can achieve good frequentist coverage in reliability contexts.

Extensions improve applicability to censored and complex reliability data.

Abstract

Especially when facing reliability data with limited information (e.g., a small number of failures), there are strong motivations for using Bayesian inference methods. These include the option to use information from physics-of-failure or previous experience with a failure mode in a particular material to specify an informative prior distribution. Another advantage is the ability to make statistical inferences without having to rely on specious (when the number of failures is small) asymptotic theory needed to justify non-Bayesian methods. Users of non-Bayesian methods are faced with multiple methods of constructing uncertainty intervals (Wald, likelihood, and various bootstrap methods) that can give substantially different answers when there is little information in the data. For Bayesian inference, there is only one method of constructing equal-tail credible intervals-but it is necessary to provide a prior distribution to fully specify the model. Much work has been done to find default prior distributions that will provide inference methods with good (and in some cases exact) frequentist coverage properties. This paper reviews some of this work and provides, evaluates, and illustrates principled extensions and adaptations of these methods to the practical realities of reliability data (e.g., non-trivial censoring).