Bayesian inference is used to calculate the posterior distribution of µ i (X n ) -y(X n ) for each datapoint, using the distribution of y(X n ) -y(X n ) over the dataset as an initial prior distribution, which is subsequently iteratively refined. The posterior distribution can be used to calculate expected values of the absolute error from variance and nonvariance defined in Eq. ( ) and Eq. ( ) for each datapoint. The contributions are then averaged across the dataset to arrive at the expected variance and nonvariance contributions to the dataset MAE.