On RKHS choices for assessing graph generators via kernel Stein statistics
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may depend on the choice of kernels in an underlying reproducing kernel Hilbert space (RKHS). Here we assess the effect of...
| Main Authors: | , , |
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| Format: | Internet publication |
| Language: | English |
| Published: |
arXiv / Cornell University
2022
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