mlsanityData to 7 Oct 2026

Stein's Method Meets Computational Statistics: A Review of Some Recent Developments

arXiv 2105.03481 ↗First posted 7 May 2021stat.ME math.ST stat.COcited by 32

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Abstract (arXiv)

Stein's method compares probability distributions through the study of a class of linear operators called Stein operators. While mainly studied in probability and used to underpin theoretical statistics, Stein's method has led to significant advances in computational statistics in recent years. The goal of this survey is to bring together some of these recent developments and, in doing so, to stimulate further research into the successful field of Stein's method and statistics. The topics we discuss include tools to benchmark and compare sampling methods such as approximate Markov chain Monte Carlo, deterministic alternatives to sampling methods, control variate techniques, parameter estimation and goodness-of-fit testing.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 May 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 4; 2024: 8; 2025: 7; 2026: 6.

32 papers.

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  1. 2024Particle Denoising Diffusion Samplercited by 63
  2. 2021Composite Goodness-of-fit Tests with Kernelscited by 16
  3. 2023Robust and Scalable Bayesian Online Changepoint Detectioncited by 16
  4. 2022Targeted Separation and Convergence with Kernel Discrepanciescited by 15
  5. 2022Generalised Bayesian Inference for Discrete Intractable Likelihoodcited by 14
  6. 2022Controlling Moments with Kernel Stein Discrepanciescited by 13
  7. 2024Stein transport for Bayesian inferencecited by 11
  8. 2022A kernel Stein test of goodness of fit for sequential modelscited by 9
  9. 2025A Dictionary of Closed-Form Kernel Mean Embeddingscited by 9
  10. 2024Conditional Bayesian Quadraturecited by 8
  11. 2024On the Robustness of Kernel Goodness-of-Fit Testscited by 7
  12. 2023Meta-learning Control Variates: Variance Reduction with Limited Datacited by 6
20 more
  1. 2023Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancycited by 4
  2. 2024Nystr"om Kernel Stein Discrepancycited by 4
  3. 2022A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spacescited by 3
  4. 2022Neural Stein critics with staged L2-regularizationcited by 2
  5. 2024Stein's method for marginals on large graphical modelscited by 2
  6. 2024The Polynomial Stein Discrepancy for Assessing Moment Convergencecited by 2
  7. 2025Fast and Scalable Score-Based Kernel Calibration Testscited by 2
  8. 2025The Minimax Lower Bound of Kernel Stein Discrepancy Estimationcited by 2
  9. 2025Robust inference using density-powered Stein operatorscited by 2
  10. 2026Efficient Learning of Stationary Diffusions with Stein-type Discrepanciescited by 2
  11. 2026Kernel Tests of Equivalencecited by 2
  12. 2026Minimax Lower Bounds of Kernel Discrepancy Estimation: MMD, HSIC, KSDcited by 2
  13. 2023Bayes Hilbert Spaces for Posterior Approximationcited by 1
  14. 2024Monte Carlo with kernel-based Gibbs measures: Guarantees for probabilistic herdingcited by 1
  15. 2025Even Faster Kernel Matrix Linear Algebra via Density Estimationcited by 1
  16. 2026TASTE: Task-Aware Out-of-Distribution Detection via Stein Operatorscited by 1
  17. 2025Stein Discrepancy for Unsupervised Domain Adaptationcited by 0
  18. 2025Quenched large deviations for Monte Carlo integration with Coulomb gasescited by 0
  19. 2026Convex-Geometric Error Bounds for Positive-Weight Kernel Quadraturecited by 0
  20. 2026Event Generation with Parallel Langevin Sampling and Learned Stein Diagnosticscited by 0