Bayesian estimation for reliability engineering: Addressing the influence of prior choice

Leonardo Leoni, Farshad Bahootoroody, Saeed Khalaj, Filippo De Carlo*, Ahmad Bahootoroody, Mohammad Mahdi Abaei

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
101 Downloads (Pure)


Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.

Original languageEnglish
Article number3349
Number of pages16
JournalInternational Journal of Environmental Research and Public Health
Issue number7
Publication statusPublished - 24 Mar 2021
MoE publication typeA1 Journal article-refereed


  • Beta-binomial failure modelling
  • Hierarchical Bayesian modelling
  • Prior information
  • Reliability analysis


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