Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design
cs.LG, cs.AI, cs.IT, cs.NE, math.IT, stat.ML
Submitted: 2026-02-06
Updated: 2026-02-06
Journal ref: Machine Learning: Science and Technology 7, 045069 (2026)
Code: https://github.com/sambklein/sbi-boed
License: http://creativecommons.org/licenses/by/4.0/
The gist: Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments.
Terminology
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 are able to match or outperform by up to 22% existing state-of-the-art approaches across standard BOED benchmarks.
Sources
- Decoupled Weight Decay Regularization
- Representation Learning with Contrastive Predictive Coding
- Sequential Neural Posterior and Likelihood Approximation
- Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design
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