mlsanityData to 14 Oct 2026

Neural Posterior Estimation with Differentiable Simulators

arXiv 2207.05636 ↗First posted 12 Jul 2022astro-ph.IM stat.MLcited by 9

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Abstract (arXiv)

Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 18 of its 21 references that are in the map. First posted on arXiv 12 Jul 2022.

Builds on

18 of 21 references.

References · 18

  1. 2015Variational Inference with Normalizing Flowscited by 2,170
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  4. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  5. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 594
  6. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 221
  7. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 219
  8. 2021Efficient and Modular Implicit Differentiationcited by 203
  9. 2017Flexible statistical inference for mechanistic models of neural dynamicscited by 163
  10. 2021Benchmarking Simulation-Based Inferencecited by 162
  11. 2020On Contrastive Learning for Likelihood-free Inferencecited by 98
  12. 2015Approximating Likelihood Ratios with Calibrated Discriminative Classifierscited by 90
6 more
  1. 2016Likelihood-free inference by ratio estimationcited by 81
  2. 2018Likelihood-free inference with emulator networkscited by 61
  3. 2018Mining gold from implicit models to improve likelihood-free inferencecited by 60
  4. 2021Smooth Normalizing Flowscited by 53
  5. 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 52
  6. 2014High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computationcited by 19

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Cited by year (2026 to 14 Oct)

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

9 papers.

  1. 2024EigenVI: score-based variational inference with orthogonal function expansionscited by 11
  2. 2024Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysiscited by 7
  3. 2024LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transportcited by 7
  4. 2025Amortized Bayesian Mixture Modelscited by 3
  5. 2023A transport approach to sequential simulation-based inferencecited by 2
  6. 2025ConDiSim: Conditional Diffusion Models for Simulation Based Inferencecited by 2
  7. 2025Fine Tuning a Simulation-Driven Estimatorcited by 1
  8. 2023Parallelized Acquisition for Active Learning using Monte Carlo Samplingcited by 0
  9. 2026Keeping Score: Adaptive, Tuning-Free Loss Weighting for Score-Augmented Neural Ratio Estimationcited by 0