mlsanityData to 7 Oct 2026

Balancing Simulation-based Inference for Conservative Posteriors

arXiv 2304.10978 ↗First posted 21 Apr 2023stat.ML cs.LG stat.MEcited by 10

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Abstract (arXiv)

Conservative inference is a major concern in simulation-based inference. It has been shown that commonly used algorithms can produce overconfident posterior approximations. Balancing has empirically proven to be an effective way to mitigate this issue. However, its application remains limited to neural ratio estimation. In this work, we extend balancing to any algorithm that provides a posterior density. In particular, we introduce a balanced version of both neural posterior estimation and contrastive neural ratio estimation. We show empirically that the balanced versions tend to produce conservative posterior approximations on a wide variety of benchmarks. In addition, we provide an alternative interpretation of the balancing condition in terms of the chi2 divergence.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 20 of its 38 references that are in the map. First posted on arXiv 21 Apr 2023.

Builds on

20 of 38 references.

References · 20

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  3. 2019Neural Spline Flowscited by 590
  4. 2019The frontier of simulation-based inferencecited by 490
  5. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
  6. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 216
  7. 2017Flexible statistical inference for mechanistic models of neural dynamicscited by 162
  8. 2018Validating Bayesian Inference Algorithms with Simulation-Based Calibrationcited by 118
  9. 2020On Contrastive Learning for Likelihood-free Inferencecited by 96
  10. 2016Likelihood-free inference by ratio estimationcited by 80
  11. 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 50
  12. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
8 more
  1. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 45
  2. 2022Variational methods for simulation-based inferencecited by 43
  3. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  4. 2022Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationcited by 27
  5. 2021Diagnostics for Conditional Density Models and Bayesian Inference Algorithmscited by 26
  6. 2020Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Settingcited by 17
  7. 2022Validation Diagnostics for SBI algorithms based on Normalizing Flowscited by 8
  8. 2022Contrastive Neural Ratio Estimation for Simulation-based Inferencecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 1; 2026: 3.

10 papers.

  1. 2023Flow Matching for Scalable Simulation-Based Inferencecited by 97
  2. 2023Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probabilitycited by 10
  3. 2023Variational Inference with Coverage Guarantees in Simulation-Based Inferencecited by 5
  4. 2024Low-Budget Simulation-Based Inference with Bayesian Neural Networkscited by 5
  5. 2025CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inferencecited by 5
  6. 2023Simulation-based Inference with the Generalized Kullback-Leibler Divergencecited by 3
  7. 2024Cost Estimation in Unit Commitment Problems Using Simulation-Based Inferencecited by 0
  8. 2026Conservative neural posterior estimation via distributionally robust trainingcited by 0
  9. 2026Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inferencecited by 0
  10. 2026Mitigating Representation Gaps in Amortized Bayesian Inference with Auxiliary Supervisioncited by 0