mlsanityData to 7 Oct 2026

Truncated proposals for scalable and hassle-free simulation-based inference

arXiv 2210.04815 ↗First posted 10 Oct 2022stat.ML cs.LGcited by 40

Not on the map. Why?

Abstract (arXiv)

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 22 of its 67 references that are in the map. First posted on arXiv 10 Oct 2022.

Builds on

22 of 67 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019Neural Spline Flowscited by 590
  3. 2018Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flowscited by 217
  4. 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 216
  5. 2017Flexible statistical inference for mechanistic models of neural dynamicscited by 162
  6. 2021Benchmarking Simulation-Based Inferencecited by 160
  7. 2020BayesFlow: Learning complex stochastic models with invertible neural networkscited by 139
  8. 2018Validating Bayesian Inference Algorithms with Simulation-Based Calibrationcited by 118
  9. 2020On Contrastive Learning for Likelihood-free Inferencecited by 96
  10. 2016Likelihood-free inference by ratio estimationcited by 80
  11. 2021Real-time gravitational-wave science with neural posterior estimationcited by 76
  12. 2018Likelihood-free inference with emulator networkscited by 61
10 more
  1. 2022Variational methods for simulation-based inferencecited by 43
  2. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  3. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  4. 2019Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inferencecited by 21
  5. 2016Inference Networks for Sequential Monte Carlo in Graphical Modelscited by 19
  6. 2017Efficient acquisition rules for model-based approximate Bayesian computationcited by 16
  7. 2021Group equivariant neural posterior estimationcited by 16
  8. 2013Diagnostic tools of approximate Bayesian computation using the coverage propertycited by 10
  9. 2020Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious timecited by 7
  10. 2016BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neurosciencecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 10; 2024: 11; 2025: 10; 2026: 8.

40 papers.

Sort:

By area:

  1. 2024All-in-one simulation-based inferencecited by 71
  2. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 45
  3. 2023JANA: Jointly Amortized Neural Approximation of Complex Bayesian Modelscited by 41
  4. 2025Simulation-Based Inference: A Practical Guidecited by 30
  5. 2024sbi reloaded: a toolkit for simulation-based inference workflowscited by 23
  6. 2023Consistency Models for Scalable and Fast Simulation-Based Inferencecited by 22
  7. 2024Real-time gravitational-wave inference for binary neutron stars using machine learningcited by 21
  8. 2023Adversarial robustness of amortized Bayesian inferencecited by 17
  9. 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationcited by 17
  10. 2025Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Modelscited by 13
  11. 2025Multifidelity Simulation-based Inference for Computationally Expensive Simulatorscited by 9
  12. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
28 more
  1. 2023Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationcited by 8
  2. 2024Compositional simulation-based inference for time seriescited by 8
  3. 2023InVAErt networks: a data-driven framework for model synthesis and identifiability analysiscited by 6
  4. 2024Preconditioned Neural Posterior Estimation for Likelihood-free Inferencecited by 6
  5. 2024Conditional diffusions for amortized neural posterior estimationcited by 6
  6. 2023Variational Inference with Coverage Guarantees in Simulation-Based Inferencecited by 5
  7. 2024A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)cited by 5
  8. 2024A Comprehensive Guide to Simulation-based Inference in Computational Biologycited by 5
  9. 2025CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inferencecited by 5
  10. 2024Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimationcited by 4
  11. 2024Demonstrating the power and flexibility of variational assumptions for amortized neural posterior estimation in environmental applicationscited by 3
  12. 2022Bayesian score calibration for approximate modelscited by 2
  13. 2023Amortized Bayesian Decision Making for simulation-based modelscited by 1
  14. 2025Fine Tuning a Simulation-Driven Estimatorcited by 1
  15. 2025Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelscited by 1
  16. 2025Causal Posterior Estimationcited by 1
  17. 2026Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Designcited by 1
  18. 2026An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learningcited by 1
  19. 2023An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimationcited by 0
  20. 2024Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoodscited by 0
  21. 2025Neural Posterior Estimation on Exponential Random Graph Models: Evaluating Bias and Implementation Challengescited by 0
  22. 2025Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysiscited by 0
  23. 2026Tokenised Flow Matching for Hierarchical Simulation Based Inferencecited by 0
  24. 2026Mixed neural posterior estimation for simulators with discrete and continuous parameterscited by 0
  25. 2026A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problemscited by 0
  26. 2026Program Synthesis for Simulation-Based Inference: Joint Model Selection and Parameter Estimationcited by 0
  27. 2026Calibrating subgrid parametrizations of single-column ocean models via simulation-based inferencecited by 0
  28. 2026A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBIcited by 0