Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
批量贝叶斯最优实验设计的 Wasserstein 梯度流
Bayesian optimal experimental design (BOED) provides a powerful, decision-theoretic framework for selecting experiments so as to maximise the expected utility of the data to be collected. In practice, however, its applicability can be limited by the difficulty of optimising the chosen utility. The expected information gain (EIG), for example, is often high-dimensional and strongly non-convex. This challenge is particularly acute in the batch setting, where multiple…