amortized variational inference

GoBOED: Goal-Driven Bayesian Optimal Experimental Design for Decision-Focused Robustness

GoBOED is a new framework for Bayesian optimal experimental design (BOED) that replaces information-gain maximization with direct optimization for a specified downstream decision objective. It combines an amortized variational posterior surrogate with a differentiable convex decision layer to enable gradient-based, decision-focused design optimization. The authors prove that GoBOED gradients are insensitive to parameter directions irrelevant to the decision goal, formally justifying why goal-driven design achieves equivalent decision quality over a wider range of experimental designs. Empirical results across source localization, epidemic management, and pharmacokinetic control show improved alignment with decision objectives compared to goal-agnostic BOED.