Document Type

Article

Publication Date

2026

DOI

10.1088/2632-2153/ae98e5

Publication Title

Machine Learning: Science and Technology

Volume

7

Issue

4

Pages

045069 (18 pp.)

Abstract

Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods match or outperform existing state-of-the-art approaches by up to 22% across standard BOED benchmarks.

Rights

© 2026 The Authors.

Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Klein, S., Neiswanger, W., Ratner, D., Kagan, M., & Gasiorowski, S. (2026). Supercharging simulation-based inference for Bayesian optimal experimental design. Machine Learning: Science and Technology, 7(4), Article 045069. https://doi.org/10.1088/2632-2153/ae98e5

ORCID

0000-0002-5747-7323 (Ratner)

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