mlsanityData to 14 Oct 2026

Probabilistic Inference and Learning with Stein's Method

arXiv 2603.07467 ↗First posted 8 Mar 2026stat.ML cs.LG math.PRcited by 5

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Abstract (arXiv)

This monograph provides a rigorous overview of theoretical and methodological aspects of probabilistic inference and learning with Stein's method. Recipes are provided for constructing Stein discrepancies from Stein operators and Stein sets, and properties of these discrepancies such as computability, separation, convergence detection, and convergence control are discussed. Further, the connection between Stein operators and Stein variational gradient descent is set out in detail. The main definitions and results are precisely stated, and references to all proofs are provided.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 18 of its 45 references that are in the map. First posted on arXiv 8 Mar 2026.

Builds on

18 of 45 references.

References · 18

  1. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 507
  2. 2016A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluationcited by 236
  3. 2017Stein Variational Gradient Descent as Gradient Flowcited by 152
  4. 2016A Kernel Test of Goodness of Fitcited by 143
  5. 2017Measuring Sample Quality with Kernelscited by 119
  6. 2015Measuring Sample Quality with Stein's Methodcited by 94
  7. 2014Control functionals for Monte Carlo integrationcited by 59
  8. 2019Minimum Stein Discrepancy Estimatorscited by 57
  9. 2018Global Non-convex Optimization with Discretized Diffusionscited by 44
  10. 2018Stein Pointscited by 43
  11. 2017A Linear-Time Kernel Goodness-of-Fit Testcited by 41
  12. 2020Stochastic Stein Discrepanciescited by 30
6 more
  1. 2019Stein Point Markov Chain Monte Carlocited by 29
  2. 2016Measuring Sample Quality with Diffusionscited by 25
  3. 2013Austerity in MCMC Land: Cutting the Metropolis-Hastings Budgetcited by 24
  4. 2018Random Feature Stein Discrepanciescited by 23
  5. 2021Sampling with Mirrored Stein Operatorscited by 17
  6. 2016Black-box Importance Samplingcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 5.

5 papers.

  1. 2026Minimax Estimation of Kernel Stein Discrepancy: Trace versus Hilbert-Schmidt Scalescited by 3
  2. 2026Closing the Approximation Gap in Simulation-free Latent SDEscited by 2
  3. 2026When Does Trimming Help Conformal Prediction? A Retained-Law Diagnostic under Calibration Contaminationcited by 1
  4. 2026TASER: Task-Aware Stein Regularisation for Geometry-Driven Robustnesscited by 0
  5. 2026Conditional Kernel Stein Discrepancycited by 0