mlsanityData to 14 Oct 2026

Likelihood-free inference by ratio estimation

arXiv 1611.10242 ↗First posted 30 Nov 2016stat.ML stat.CO stat.MEcited by 81

Not on the map. Why?

Abstract (arXiv)

We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference in the absence of a likelihood function. The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution. In another popular approach called approximate Bayesian computation, the inference is performed by identifying parameter values for which the summary statistics of the simulated data are close to those of the observed data. Synthetic likelihood is easier to use as no measure of `closeness' is required but the Gaussianity assumption is often limiting. Moreover, both approaches require judiciously chosen summary statistics. We here present an alternative inference approach that is as easy to use as synthetic likelihood but not as restricted in its assumptions, and that, in a natural way, enables automatic selection of relevant summary statistic from a large set of candidates. The basic idea is to frame the problem of estimating the posterior as a problem of estimating the ratio between the data generating distribution and the marginal distribution. This problem can be solved by logistic regression, and including regularising penalty terms enables automatic selection of the summary statistics relevant to the inference task. We illustrate the general theory on canonical examples and employ it to perform inference for challenging stochastic nonlinear dynamical systems and high-dimensional summary statistics.

Key facts. Cited by 81 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2016.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 17; 2023: 19; 2024: 6; 2025: 16; 2026: 8.

81 papers.

Sort:

By area:

  1. 2021Benchmarking Simulation-Based Inferencecited by 162
  2. 2023Modern Bayesian Experimental Designcited by 125
  3. 2023Flow Matching for Scalable Simulation-Based Inferencecited by 101
  4. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  5. 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
  6. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  7. 2022Robust Neural Posterior Estimation and Statistical Model Criticismcited by 46
  8. 2023Sampling-Based Accuracy Testing of Posterior Estimators for General Inferencecited by 46
  9. 2022Investigating the Impact of Model Misspecification in Neural Simulation-based Inferencecited by 45
  10. 2022Variational methods for simulation-based inferencecited by 43
  11. 2022Truncated proposals for scalable and hassle-free simulation-based inferencecited by 41
  12. 2024Neural Methods for Amortized Inferencecited by 39
38 more
  1. 2022GATSBI: Generative Adversarial Training for Simulation-Based Inferencecited by 36
  2. 2021Featurized Density Ratio Estimationcited by 33
  3. 2025Simulation-Based Inference: A Practical Guidecited by 32
  4. 2021Sequential Neural Posterior and Likelihood Approximationcited by 30
  5. 2021Truncated Marginal Neural Ratio Estimationcited by 30
  6. 2021A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithfulcited by 28
  7. 2022Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimationcited by 28
  8. 2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 27
  9. 2021BORE: Bayesian Optimization by Density-Ratio Estimationcited by 26
  10. 2021Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learningcited by 24
  11. 2023TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Seriescited by 18
  12. 2023Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationcited by 17
  13. 2022Black-box Bayesian inference for economic agent-based modelscited by 13
  14. 2023Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networkscited by 13
  15. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
  16. 2021Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inferencecited by 11
  17. 2021Unifying Likelihood-free Inference with Black-box Optimization and Beyondcited by 11
  18. 2022Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transportcited by 11
  19. 2022Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networkscited by 11
  20. 2025Dequantified Diffusion-Schr"odinger Bridge for Density Ratio Estimationcited by 11
  21. 2023Balancing Simulation-based Inference for Conservative Posteriorscited by 10
  22. 2022Neural Posterior Estimation with Differentiable Simulatorscited by 9
  23. 2025Multifidelity Simulation-based Inference for Computationally Expensive Simulatorscited by 9
  24. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
  25. 2022Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimationcited by 8
  26. 2023Misspecification-robust Sequential Neural Likelihood for Simulation-based Inferencecited by 8
  27. 2023Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimationcited by 8
  28. 2022Calibrating Agent-based Models to Microdata with Graph Neural Networkscited by 7
  29. 2022Contrastive Neural Ratio Estimation for Simulation-based Inferencecited by 7
  30. 2022Statistical applications of contrastive learningcited by 5
  31. 2023Variational Inference with Coverage Guarantees in Simulation-Based Inferencecited by 5
  32. 2023On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Designcited by 5
  33. 2023Direct Amortized Likelihood Ratio Estimationcited by 5
  34. 2025Multilevel neural simulation-based inferencecited by 5
  35. 2021ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Modelscited by 4
  36. 2021Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMCcited by 3
  37. 2023Simulation-based Inference with the Generalized Kullback-Leibler Divergencecited by 3
  38. 2024Embed and Emulate: Contrastive representations for simulation-based inferencecited by 3

50 most cited of 81 All 81 (JSON)