SBI-BOED trains a normalizing-flow likelihood surrogate while optimizing experimental designs via InfoNCE-style mutual information bounds, and works when the simulator is non-differentiable.
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Optimizing Likelihoods via Mutual Information: Bridging Simulation-Based Inference and Bayesian Optimal Experimental Design
SBI-BOED trains a normalizing-flow likelihood surrogate while optimizing experimental designs via InfoNCE-style mutual information bounds, and works when the simulator is non-differentiable.