Random Feature Stein Discrepancies

@article{Huggins2018RandomFS,
  title={Random Feature Stein Discrepancies},
  author={Jonathan H. Huggins and Lester W. Mackey},
  journal={CoRR},
  year={2018},
  volume={abs/1806.07788}
}
Computable Stein discrepancies have been deployed for a variety of applications, including sampler selection in posterior inference, approximate Bayesian inference, and goodness-of-fit testing. Existing convergence-determining Stein discrepancies admit strong theoretical guarantees but suffer from a computational cost that grows quadratically in the sample… CONTINUE READING