Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
Bayesian experimental design (BED) has traditionally been based on maximising expected uncertainty reductions from prior to posterior. A major shortfall of this approach is that it leads to doubly intractable objectives that are difficult to optimise, while customising them to particular downstream tasks of interest can also be difficult. Following first principles decision theory, we demonstrate that BED can alternatively be formulated in terms of an expected future loss (EFL) on downstream actions, providing a simple and naturally task-driven framework. Critically, we then show that all such EFLs can be rearranged into singly intractable objectives that can be jointly optimised with res...
| Authors | Tom Rossa, Angus Phillips, Tom Rainforth |
|---|---|
| Primary category | stat.ML |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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