Computationally efficient goodness-of-fit tests through kernelized Stein discrepancy
This research introduces a new statistical test called SKSD, which efficiently checks if data fits a model. It combines score-based testing with distance metrics using kernel functions to create a powerful tool. This method works well even when the model's likelihood is too complex to calculate directly.