mlsanityData to 7 Oct 2026

Likelihood-free inference with emulator networks

arXiv 1805.09294 ↗First posted 23 May 2018stat.ML cs.LGcited by 61

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Abstract (arXiv)

Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data -- both local emulators which approximate the likelihood for specific observed data, as well as global ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on high-dimensional problems which are challenging for conventional ABC approaches.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2018.

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Citing papers per year: 2021: 6; 2022: 12; 2023: 13; 2024: 8; 2025: 15; 2026: 7.

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  1. 2021Benchmarking Simulation-Based Inferencecited by 160
  2. 2023Flow Matching for Scalable Simulation-Based Inferencecited by 97
  3. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
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  6. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 45
  7. 2022Variational methods for simulation-based inferencecited by 43
  8. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  9. 2022Truncated proposals for scalable and hassle-free simulation-based inferencecited by 40
  10. 2024Neural Methods for Amortized Inferencecited by 36
  11. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  12. 2025Simulation-Based Inference: A Practical Guidecited by 30
38 more
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  5. 2022Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimizationcited by 14
  6. 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Modelscited by 13
  7. 2021Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inferencecited by 10
  8. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 10
  9. 2022Approximate Bayesian Computation with Domain Expert in the Loopcited by 9
  10. 2022Neural Posterior Estimation with Differentiable Simulatorscited by 9
  11. 2024Sourcerer: Sample-based Maximum Entropy Source Distribution Estimationcited by 9
  12. 2025Multifidelity Simulation-based Inference for Computationally Expensive Simulatorscited by 9
  13. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
  14. 2021Deep Bayesian Active Learning for Accelerating Stochastic Simulationcited by 7
  15. 2022Contrastive Neural Ratio Estimation for Simulation-based Inferencecited by 7
  16. 2023A generative flow for conditional sampling via optimal transportcited by 7
  17. 2026BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Pythoncited by 7
  18. 2023Towards black-box parameter estimationcited by 6
  19. 2022Generative Bayesian Inference with GANscited by 5
  20. 2022Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationcited by 5
  21. 2025Multilevel neural simulation-based inferencecited by 5
  22. 2021Robust and integrative Bayesian neural networks for likelihood-free parameter inferencecited by 4
  23. 2024Simulation-Based Inference with Quantile Regressioncited by 4
  24. 2023Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inferencecited by 3
  25. 2024Embed and Emulate: Contrastive representations for simulation-based inferencecited by 3
  26. 2025PriorGuide: Test-Time Prior Adaptation for Simulation-Based Inferencecited by 3
  27. 2023Optimal simulation-based Bayesian decisionscited by 2
  28. 2024Generative vs. Discriminative modeling under the lens of uncertainty quantificationcited by 2
  29. 2024Full-waveform earthquake source inversion using simulation-based inferencecited by 2
  30. 2024Cosmological Analysis with Calibrated Neural Quantile Estimation and Approximate Simulatorscited by 2
  31. 2024Conformal Calibration of Statistical Confidence Setscited by 2
  32. 2025Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuningcited by 2
  33. 2023Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inferencecited by 1
  34. 2025Synthesizing 3D Abstractions by Inverting Procedural Buildings with Transformerscited by 1
  35. 2025Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelscited by 1
  36. 2025Simulation-Efficient Cosmological Inference with Multi-Fidelity SBIcited by 1
  37. 2026Latent Target Score Matching, with an application to Simulation-Based Inferencecited by 1
  38. 2026An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learningcited by 1

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