Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design
A risk-aware framework that composes risk at three levels, each generalizing an ingredient of classical $I- and $G-optimal design: a deviation measure of the posterior predictive uncertainty, a risk measure across the prediction domain, and a risk measure over datasets.