AI RESEARCH
The Polynomial Stein Discrepancy for Assessing Moment Convergence
arXiv CS.LG
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ArXi:2412.05135v2 Announce Type: replace-cross We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample quality like the effective sample size are not appropriate for scalable Bayesian sampling algorithms, such as stochastic gradient Langevin dynamics, that are asymptotically biased. Instead, the gold standard is to use the kernel Stein Discrepancy (KSD), which is itself not scalable given its quadratic cost in the number of samples.