mlsanityData to 14 Oct 2026

A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation

arXiv 1602.03253 ↗First posted 10 Feb 2016stat.MLcited by 236

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Cited by 32 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-of-fit tests that are widely applicable for complex and high dimensional distributions, even for those with computationally intractable normalization constants. Both theoretical and empirical properties of our methods are studied thoroughly.

Key facts. Cited by 236 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2016.

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Citing papers per year: 2020: 1; 2021: 46; 2022: 47; 2023: 38; 2024: 31; 2025: 36; 2026: 37.

236 papers.

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38 more
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  18. 2023SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identitycited by 23
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