Constrained Bayesian Experimental Design via Online Planning

• URL: http://arxiv.org/abs/2605.26990v1
• Date: Tue, 26 May 2026 13:13:28 GMT
• Title: Constrained Bayesian Experimental Design via Online Planning
• Abstract summary: Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments.<n>Existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due to budget limitations, varying costs, or physical constraints.<n>We introduce a novel approach to BED that enables constrained optimization of experimental designs by combining offline pre-training of an amortized policy and a posterior network with online multi-step lookahead planning.
• Score: 26.738280001310937
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due to budget limitations, varying costs, or physical constraints that restrict how designs evolve over time. In this paper, we introduce a novel approach to BED that enables constrained optimization of experimental designs by combining offline pre-training of an amortized policy and a posterior network with online multi-step lookahead planning using scenario trees. We empirically demonstrate that our method yields substantially more informative design sequences than existing methods across a range of constrained BED tasks, while incurring only a modest additional computational overhead.

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