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Automatic Posterior Transformation for Likelihood-Free Inference

arXiv 1905.07488 ↗First posted 17 May 2019cs.LG stat.MLcited by 216

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Abstract (arXiv)

How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators. However, existing methods are limited to a narrow range of proposal distributions or require importance weighting that can limit performance in practice. Here we present automatic posterior transformation (APT), a new sequential neural posterior estimation method for simulation-based inference. APT can modify the posterior estimate using arbitrary, dynamically updated proposals, and is compatible with powerful flow-based density estimators. It is more flexible, scalable and efficient than previous simulation-based inference techniques. APT can operate directly on high-dimensional time series and image data, opening up new applications for likelihood-free inference.

Key facts. Cited by 216 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2019.

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Citing papers per year: 2021: 24; 2022: 29; 2023: 38; 2024: 40; 2025: 41; 2026: 44.

216 papers.

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  1. 2021Benchmarking Simulation-Based Inferencecited by 160
  2. 2023Flow Matching for Scalable Simulation-Based Inferencecited by 97
  3. 2021Real-time gravitational-wave science with neural posterior estimationcited by 76
  4. 2024All-in-one simulation-based inferencecited by 71
  5. 2022From Denoising Diffusions to Denoising Markov Modelscited by 58
  6. 2021Robot Learning from Randomized Simulations: A Reviewcited by 56
  7. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  8. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 48
  9. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  10. 2022Compositional Score Modeling for Simulation-based Inferencecited by 46
  11. 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 45
  12. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 45
38 more
  1. 2022Investigating the Impact of Model Misspecification in Neural Simulation-based Inferencecited by 44
  2. 2022Variational methods for simulation-based inferencecited by 43
  3. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  4. 2022Truncated proposals for scalable and hassle-free simulation-based inferencecited by 40
  5. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  6. 2024Neural Methods for Amortized Inferencecited by 36
  7. 2023BayesFlow: Amortized Bayesian Workflows With Neural Networkscited by 32
  8. 2021Sequential Neural Posterior and Likelihood Approximationcited by 30
  9. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  10. 2025Simulation-Based Inference: A Practical Guidecited by 30
  11. 2021A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithfulcited by 27
  12. 2022Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationcited by 27
  13. 2024sbi reloaded: a toolkit for simulation-based inference workflowscited by 23
  14. 2023Consistency Models for Scalable and Fast Simulation-Based Inferencecited by 22
  15. 2024Amortized Probabilistic Conditioning for Optimization, Simulation and Inferencecited by 22
  16. 2024Real-time gravitational-wave inference for binary neutron stars using machine learningcited by 21
  17. 2022Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombinationcited by 20
  18. 2023Sensitivity-Aware Amortized Bayesian Inferencecited by 20
  19. 2025Position: The Future of Bayesian Prediction Is Prior-Fittedcited by 19
  20. 2022On minimax density estimation via measure transportcited by 18
  21. 2021HNPE: Leveraging Global Parameters for Neural Posterior Estimationcited by 17
  22. 2023Adversarial robustness of amortized Bayesian inferencecited by 17
  23. 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationcited by 17
  24. 2023L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inferencecited by 17
  25. 2024Diffusion posterior sampling for simulation-based inference in tall data settingscited by 17
  26. 2021Group equivariant neural posterior estimationcited by 16
  27. 2022Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimizationcited by 14
  28. 2023Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inferencecited by 14
  29. 2021Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimationcited by 13
  30. 2022Black-box Bayesian inference for economic agent-based modelscited by 13
  31. 2023Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inferencecited by 13
  32. 2023Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networkscited by 13
  33. 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Modelscited by 13
  34. 2024Amortized Bayesian Multilevel Modelscited by 12
  35. 2025Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptationcited by 12
  36. 2022Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transportcited by 11
  37. 2021Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inferencecited by 10
  38. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 10

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