Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design

arXiv:2602.06900 · cs.LG, cs.AI, cs.IT, cs.NE, math.IT, stat.ML · Submitted 2026-02-06 · Read on arXiv

cs.LG, cs.AI, cs.IT, cs.NE, math.IT, stat.ML

Submitted: 2026-02-06

Updated: 2026-02-06

Code: https://github.com/sambklein/sbi-boed

Terminology

• Benchmark — 20× in this paper · explained in 2 episodes
• Normalizing Flow — 14× in this paper · explained in 3 episodes
• Normalizing Flows — 11× in this paper · explained in 4 episodes
• Simulation-based Inference — 10× in this paper · explained in 2 episodes
• Bayesian Optimal Experimental Design — 7× in this paper
• Information Gain — 5× in this paper · explained in 2 episodes
• Simulation-Based Inference (SBI) — 4× in this paper · explained in 3 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Constant Elasticity of Substitution — 2× in this paper
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Acquisition Function — 1× in this paper · explained in 1 episode
• Active Learning — 1× in this paper · explained in 6 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Amortization — 1× in this paper · explained in 1 episode
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Neural posterior estimation (NPE) — 1× in this paper · explained in 2 episodes
• Predictive Coding — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• US Department of Energy — 1× in this paper

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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