Amortized Inference for Sampling Distributions Where the Bootstrap Fails

arXiv:2607.16666 · stat.ME, stat.ML · Submitted 2026-07-18 · Read on arXiv

Akash Deep

stat.ME, stat.ML

Submitted: 2026-07-18

Comments: 6 figures, 10 tables. Code and reproducible experiments: https://github.com/akashdeepo/Bootstrap_Backprop

Code: https://github.com/akashdeepo/Bootstrap_Backprop

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Bayesian Inference — 3× in this paper · explained in 6 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Calibration — 3× in this paper · explained in 9 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Regret — 3× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Uncertainty Quantification — 2× in this paper · explained in 9 episodes
• Wasserstein Distance — 2× in this paper · explained in 6 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Degeneracy — 1× in this paper · explained in 4 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Generator — 1× in this paper · explained in 2 episodes
• Monotonicity — 1× in this paper · explained in 2 episodes
• Simulation-Based Inference — 1× in this paper · explained in 3 episodes
• Transformers — 1× in this paper · explained in 2 episodes

Sources

• Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference
• Deep Bootstrap for Bayesian Inference
• Generative Multiple-purpose Sampler for Weighted M-estimation

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