Constrained Bayesian Experimental Design via Online Planning

2026-05-26

AI Summary

This work addresses the limited adaptability of Bayesian experimental design under dynamic constraints—such as budget, cost, or physical limitations—by introducing a novel approach that integrates offline amortized inference with online multi-step lookahead planning. The method uniquely combines a pretrained amortized posterior policy with scenario-tree-based online planning to efficiently optimize sequences of experiments while respecting evolving constraints. By jointly leveraging amortized Bayesian inference, scenario tree construction, and constrained optimization, the proposed framework substantially enhances the information gain of selected experiments across diverse constrained tasks, achieving high efficiency and strong adaptability with only modest additional computational overhead.

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.

Problem

• Bayesian experimental design
• dynamic constraints
• sequential experiments
• budget limitations
• physical constraints

Innovation

• Constrained Bayesian Experimental Design
• Online Planning
• Scenario Trees
• Amortized Policy
• Sequential Experimentation