mlsanityData to 14 Oct 2026

Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design

arXiv 2502.08004 ↗First posted 11 Feb 2025stat.ML cs.LGcited by 2

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Abstract (arXiv)

Simulation-based inference (SBI) is a method to perform inference on a variety of complex scientific models with challenging inference (inverse) problems. Bayesian Optimal Experimental Design (BOED) aims to efficiently use experimental resources to make better inferences. Various stochastic gradient-based BOED methods have been proposed as an alternative to Bayesian optimization and other experimental design heuristics to maximize information gain from an experiment. We demonstrate a link via mutual information bounds between SBI and stochastic gradient-based variational inference methods that permits BOED to be used in SBI applications as SBI-BOED. This link allows simultaneous optimization of experimental designs and optimization of amortized inference functions. We evaluate the pitfalls of naive design optimization using this method in a standard SBI task and demonstrate the utility of a well-chosen design distribution in BOED. We compare this approach on SBI-based models in real-world simulators in epidemiology and biology, showing notable improvements in inference.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 42 of its 56 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

42 of 56 references.

References · 42

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30 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025JADAI: Jointly Amortizing Adaptive Design and Bayesian Inferencecited by 4
  2. 2026Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Designcited by 1