 The bias-variance decomposition of mean squared error is rooted in frequentist statistics, where the objective is to compute an estimate \(h(X)\) of the true parameter \(r(X)\) with respect to data \(\mathcal{D}\) about the relationship between X and Y. Here the parameter \(r(X)\) characterizing the truth about Y is assumed to be fixed and the data \(\mathcal{D}\) is treated as a random quantity, which is exactly the reverse of Bayesian statistics. What this means is that the data set \(\mathcal{D}\) is interpreted to be one among many possible data sets of the same dimension generated by the true model, the deterministic process \(r(X)\).