The paper Specifying Prior Distributions in Reliability Applications (Tian et al. (2023)) mainly provides an overview of methods for selecting noninformative prior distributions for parameters of basic lifetime distributions, as often used in reliability analyses. This discussion raises some related issues and comments on opportunities beyond basic Bayesian statistical methods which may be useful in reliability scenarios. The main emphasis in this discussion is on practical reliability analyses with few data available, where there is often need for informative priors rather than for non-informative priors, in order to take expert judgement into account. Furthermore, while rather abstract considerations of non-informativeness of prior distributions is of theoretic interest, in most practical scenarios one aims at decision support, and the influence of assumed priors on the final decisions should be considered, ideally with robustness of the final decision with regard to all priors which are deemed to be reasonable.
Coolen, F. (2023). Discussion of specifying prior distributions in reliability applications. Applied Stochastic Models in Business and Industry, https://doi.org/10.1002/asmb.2799