# Bayesian Nonparametric System Reliability using Sets of Priors

arXiv: https://arxiv.org/abs/1602.01650 · HTML: https://mlsanity.com/paper/1602.01650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2016.

## Built on by (most cited first, 3 of 3)

- [Bayesian Learning for the Robust Verification of Autonomous Robots](https://mlsanity.com/paper/2303.08476/) (2023)
- [A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models](https://mlsanity.com/paper/2511.00527/) (2025)
- [A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland](https://mlsanity.com/paper/2603.23134/) (2026)

## Abstract (arXiv)

An imprecise Bayesian nonparametric approach to system reliability with multiple types of components is developed. This allows modelling partial or imperfect prior knowledge on component failure distributions in a flexible way through bounds on the functioning probability. Given component level test data these bounds are propagated to bounds on the posterior predictive distribution for the functioning probability of a new system containing components exchangeable with those used in testing. The method further enables identification of prior-data conflict at the system level based on component level test data. New results on first-order stochastic dominance for the Beta-Binomial distribution make the technique computationally tractable. Our methodological contributions can be immediately used in applications by reliability practitioners as we provide easy to use software tools.