mlsanityData to 14 Oct 2026

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

arXiv 1805.07226 ↗First posted 18 May 2018stat.ML cs.LGcited by 221

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Abstract (arXiv)

We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible. SNL trains an autoregressive flow on simulated data in order to learn a model of the likelihood in the region of high posterior density. A sequential training procedure guides simulations and reduces simulation cost by orders of magnitude. We show that SNL is more robust, more accurate and requires less tuning than related neural-based methods, and we discuss diagnostics for assessing calibration, convergence and goodness-of-fit.

Key facts. Cited by 221 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 May 2018.

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Citing papers per year: 2021: 22; 2022: 27; 2023: 39; 2024: 47; 2025: 41; 2026: 45.

221 papers.

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  1. 2021Benchmarking Simulation-Based Inferencecited by 162
  2. 2023Flow Matching for Scalable Simulation-Based Inferencecited by 101
  3. 2024All-in-one simulation-based inferencecited by 74
  4. 2021Robot Learning from Randomized Simulations: A Reviewcited by 63
  5. 2022From Denoising Diffusions to Denoising Markov Modelscited by 58
  6. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  7. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 49
  8. 2022Compositional Score Modeling for Simulation-based Inferencecited by 48
  9. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  10. 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 46
  11. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 46
  12. 2022Investigating the Impact of Model Misspecification in Neural Simulation-based Inferencecited by 45
38 more
  1. 2022Variational methods for simulation-based inferencecited by 43
  2. 2022Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrapcited by 41
  3. 2022Truncated proposals for scalable and hassle-free simulation-based inferencecited by 41
  4. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  5. 2024Neural Methods for Amortized Inferencecited by 39
  6. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  7. 2023BayesFlow: Amortized Bayesian Workflows With Neural Networkscited by 32
  8. 2025Simulation-Based Inference: A Practical Guidecited by 32
  9. 2021Sequential Neural Posterior and Likelihood Approximationcited by 30
  10. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  11. 2021A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithfulcited by 28
  12. 2022Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationcited by 28
  13. 2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 27
  14. 2024Addressing Misspecification in Simulation-based Inference through Data-driven Calibrationcited by 27
  15. 2023Conditional Optimal Transport on Function Spacescited by 26
  16. 2024Dynamic Conditional Optimal Transport through Simulation-Free Flowscited by 25
  17. 2024Amortized Probabilistic Conditioning for Optimization, Simulation and Inferencecited by 23
  18. 2024sbi reloaded: a toolkit for simulation-based inference workflowscited by 23
  19. 2024Diffusion posterior sampling for simulation-based inference in tall data settingscited by 19
  20. 2024Conditional sampling within generative diffusion modelscited by 19
  21. 2022On minimax density estimation via measure transportcited by 18
  22. 2023L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inferencecited by 18
  23. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  24. 2023Adversarial robustness of amortized Bayesian inferencecited by 17
  25. 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationcited by 17
  26. 2021Group equivariant neural posterior estimationcited by 16
  27. 2022Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimizationcited by 15
  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. 2023Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networkscited by 13
  32. 2025Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptationcited by 13
  33. 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Modelscited by 13
  34. 2024Flexible Tails for Normalizing Flowscited by 12
  35. 2026A Probabilistic Framework for LLM-Based Model Discoverycited by 12
  36. 2021Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inferencecited by 11
  37. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 11
  38. 2022Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transportcited by 11

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