Bayesian Experimental Design via Score Matching
Policy-based approaches to Bayesian experimental design (BED) allow the learning of deep policy networks that adaptively make intelligent design decisions based on previously collected data. However, the training of such policies is often held back by a fundamental challenge: the double intractability of the expected information gain (EIG). This necessitates expensive or complex approximations that restrict the effort one can invest in optimising the policy itself. To address this, we show that the double intractability of the EIG can be isolated from the policy learning by first solving a score matching problem that is independent of the policy used, then using the learned score approxim...
| Authors | Angus Phillips, Gavin Kerrigan, Tom Rainforth |
|---|---|
| Primary category | stat.ML |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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