mlsanityData to 14 Oct 2026

HNPE: Leveraging Global Parameters for Neural Posterior Estimation

arXiv 2102.06477 ↗First posted 12 Feb 2021stat.ML cs.LG q-bio.QMcited by 19

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Abstract (arXiv)

Inferring the parameters of a stochastic model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e. when distinct sets of parameters yield identical observations. This arises in many practical situations, such as when inferring the distance and power of a radio source (is the source close and weak or far and strong?) or when estimating the amplifier gain and underlying brain activity of an electrophysiological experiment. In this work, we present hierarchical neural posterior estimation (HNPE), a novel method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters. Our method extends recent developments in simulation-based inference (SBI) based on normalizing flows to Bayesian hierarchical models. We validate quantitatively our proposal on a motivating example amenable to analytical solutions and then apply it to invert a well known non-linear model from computational neuroscience, using both simulated and real EEG data.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 15 of its 45 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

15 of 45 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2017Deep Setscited by 1,421
  4. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,323
  5. 2017Masked Autoregressive Flow for Density Estimationcited by 684
  6. 2019Neural Spline Flowscited by 596
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  8. 2018Pyro: Deep Universal Probabilistic Programmingcited by 380
  9. 2015MADE: Masked Autoencoder for Distribution Estimationcited by 307
  10. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 219
  11. 2020On Contrastive Learning for Likelihood-free Inferencecited by 98
  12. 2016Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimationcited by 52
3 more
  1. 2019Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learningcited by 15
  2. 2019Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEscited by 7
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 5; 2025: 2; 2026: 8.

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  1. 2024Diffusion posterior sampling for simulation-based inference in tall data settingscited by 19
  2. 2023L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inferencecited by 18
  3. 2024Amortized Bayesian Multilevel Modelscited by 14
  4. 2023Hierarchical Neural Simulation-Based Inference Over Event Ensemblescited by 9
  5. 2022Validation Diagnostics for SBI algorithms based on Normalizing Flowscited by 8
  6. 2023Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationcited by 8
  7. 2024A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)cited by 7
  8. 2024Simulation-based Inference with the Python Package sbijaxcited by 6
  9. 2025CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inferencecited by 6
  10. 2024Embed and Emulate: Contrastive representations for simulation-based inferencecited by 3
  11. 2025metabeta -- A fast neural model for Bayesian mixed-effects regressioncited by 2
  12. 2026Tokenised Flow Matching for Hierarchical Simulation Based Inferencecited by 1
7 more
  1. 2026It Just Takes Two: Scaling Amortized Inference to Large Setscited by 1
  2. 2026Theoretical guidelines for annealed Langevin dynamics in compositional simulation-based inferencecited by 1
  3. 2026Amortized Bayesian Inference on Multilevel Models of Arbitrary Structurecited by 1
  4. 2026Mixed neural posterior estimation for simulators with discrete and continuous parameterscited by 0
  5. 2026GenSBI: Generative Methods for Simulation-Based Inference in JAXcited by 0
  6. 2026A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBIcited by 0
  7. 2026Semi-Amortized Bayesian Inference with Reusable Local Posteriorscited by 0