Fig. 2.

Given data $\{{\mathit{x}}_i,{\mathit{y}}_i{\}}_{i=1}^n$, a Bayesian model $\mathcal{M}$ is constructed. For each functional $h(\tilde{\mathit{y}})$ and using model $\mathcal{M}$, multiple parametrized actions $\mathcal{A}$ are optimized, evaluated, and compared. The optimal parameters ${\widehat{\delta }}_{\mathcal{A}}$ are used to compute point predictions $g(\tilde{\mathit{x}};{\widehat{\delta }}_{\mathcal{A}})$ of $h(\tilde{\mathit{y}})$ at $\tilde{\mathit{x}}$.