Computationally efficient goodness-of-fit tests through kernelized Stein discrepancy

Excerpt

arXiv:2512.20007v3 Announce Type: replace-cross Abstract: Models with intractable normalizing constants are widely used in statistics and machine learning. Assessing the adequacy of such models poses significant challenges: obtaining samples from the fitted model often requires sophisticated sampling algorithms. Moreover, model fitting sometimes requires iterative numerical optimization, making bootstrap procedures that require repeated refitting computationally expensive. In this paper, we leve