Bayesian Regression over Sparse Fatigue Crack Growth Data for Nuclear Piping

This work looks into the use of Bayesian Regression technique to quantify the uncertainty over a set of sparse fatigue crack growth data that is obtained through a 4-point bending test of a carbon-steel nuclear piping. As a form of validation, the Interval Predictor Model is used which also serves as a way to provide a "Reliability Certificate" for the Bayesian Regression results