Paper: Introduction to Simulation-Based Inference with ML
RexDouglass · x · 2026-08-24
This paper, "An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning," provides a comprehensive overview of SBI methods.
Key points include:
- Frameworks: Compares Bayesian and frequentist statistical frameworks for solving inverse problems.
- Methods: Details ML-based SBI techniques like Neural Posterior Estimation (NPE) and Neural Likelihood Estimation (NLE) for parameter estimation.
- Applications: Shows applicability to Empirical Bayes or unfolding tasks.
- Validation: Discusses how to validate inference results and the limitations of current SBI methods.
The work spans Machine Learning, Cosmology, and High Energy Physics.
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