mlsanityData to 14 Oct 2026

Bayesian Synthetic Likelihood

arXiv 2305.05120 ↗First posted 9 May 2023stat.ME stat.COcited by 29

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Abstract (arXiv)

Bayesian statistics is concerned with conducting posterior inference for the unknown quantities in a given statistical model. Conventional Bayesian inference requires the specification of a probabilistic model for the observed data, and the construction of the resulting likelihood function. However, sometimes the model is so complicated that evaluation of the likelihood is infeasible, which renders exact Bayesian inference impossible. Bayesian synthetic likelihood (BSL) is a posterior approximation procedure that can be used to conduct inference in situations where the likelihood is intractable, but where simulation from the model is straightforward. In this entry, we give a high-level presentation of BSL, and its extensions aimed at delivering scalable and robust posterior inferences.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 May 2023.

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Citing papers per year: 2023: 4; 2024: 10; 2025: 12; 2026: 3.

29 papers.

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  1. 2023Learning Robust Statistics for Simulation-based Inference under Model Misspecificationcited by 48
  2. 2025Simulation-based Bayesian inference under model misspecificationcited by 9
  3. 2024PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimationcited by 8
  4. 2024Preconditioned Neural Posterior Estimation for Likelihood-free Inferencecited by 6
  5. 2024A Comprehensive Guide to Simulation-based Inference in Computational Biologycited by 6
  6. 2024Cost-aware simulation-based inferencecited by 6
  7. 2025Multilevel neural simulation-based inferencecited by 5
  8. 2024A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Liftingcited by 3
  9. 2023A transport approach to sequential simulation-based inferencecited by 2
  10. 2024A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Designcited by 2
  11. 2024Generalized Bayesian deep reinforcement learningcited by 2
  12. 2025Simulation-based inference for stochastic nonlinear mixed-effects models with applications in systems biologycited by 2
17 more
  1. 2023Wasserstein Gaussianization and Efficient Variational Bayes for Robust Bayesian Synthetic Likelihoodcited by 1
  2. 2024Structured Regularization for Constrained Optimization on the SPD Manifoldcited by 1
  3. 2024Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihoodcited by 1
  4. 2025Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Modelscited by 1
  5. 2025A Review of Diffusion-based Simulation-Based Inference: Foundations and Applications in Non-Ideal Data Scenarioscited by 1
  6. 2023An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimationcited by 0
  7. 2024Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoodscited by 0
  8. 2025Neural Bayes estimation and selection for complex bivariate extremal dependence modelscited by 0
  9. 2025Measuring the right thing: justifying metrics in AI impact assessmentscited by 0
  10. 2025Flexible Selective Inference with Flow-based Transport Mapscited by 0
  11. 2025Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysiscited by 0
  12. 2025Misspecification-robust amortised simulation-based inference using variational methodscited by 0
  13. 2025Fast and Robust Simulation-Based Inference With Optimization Monte Carlocited by 0
  14. 2025Prequential posteriorscited by 0
  15. 2026Preconditioned Robust Neural Posterior Estimation for Misspecified Simulatorscited by 0
  16. 2026Amortized Neural Clustering of Time Series based on Statistical Featurescited by 0
  17. 2026Amortized ratio-estimation importance sampling and localized simulation-based calibration for intractable likelihoodscited by 0