mlsanityData to 14 Oct 2026

Hierarchical Neural Simulation-Based Inference Over Event Ensembles

arXiv 2306.12584 ↗First posted 21 Jun 2023stat.ML astro-ph.IM cs.LGcited by 9

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Abstract (arXiv)

When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where "local" parameters impact individual events and "global" parameters influence the entire dataset. We introduce practical approaches for frequentist and Bayesian dataset-wide probabilistic inference in cases where the likelihood is intractable, but simulations can be realized via a hierarchical forward model. We construct neural estimators for the likelihood(-ratio) or posterior and show that explicitly accounting for the model's hierarchical structure can lead to significantly tighter parameter constraints. We ground our discussion using case studies from the physical sciences, focusing on examples from particle physics and cosmology.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 21 Jun 2023.

Builds on

20 of 42 references.

References · 20

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8 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 5.

9 papers.

  1. 2024Amortized Bayesian Multilevel Modelscited by 14
  2. 2025Amortized Bayesian Mixture Modelscited by 3
  3. 2025metabeta -- A fast neural model for Bayesian mixed-effects regressioncited by 2
  4. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  5. 2026Tokenised Flow Matching for Hierarchical Simulation Based Inferencecited by 1
  6. 2026It Just Takes Two: Scaling Amortized Inference to Large Setscited by 1
  7. 2026Amortized Bayesian Inference on Multilevel Models of Arbitrary Structurecited by 1
  8. 2026Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarioscited by 0
  9. 2026Semi-Amortized Bayesian Inference with Reusable Local Posteriorscited by 0