BAYESIAN INFERENCE TO ESTIMATE RANDOM FAILURE PROBABILITY.

This study introduces a process based on Bayesian inference, enhancing the accuracy of random failure probability estimation, outlined in a detailed six-step procedure. This method focuses on comprehensive data analysis and precise probability estimations, proving particularly beneficial for limited datasets. Applied to brake disc random failure probability assessment, our approach's results were compared with those obtained through Maximum Likelihood Estimation (MLE) across various specimen sizes. This comparative analysis included both graphical and statistical evaluations. The experimental findings demonstrate that our Bayesian inference-based process effectively addresses the challenges posed by small datasets, significantly enhancing estimation accuracy. This methodology is especially advantageous in scenarios where data collection is difficult, providing reliability engineers with an essential framework for leveraging prior information to improve risk management in diverse industrial applications. [ABSTRACT FROM AUTHOR]