Kernelized Complete Conditional Stein Discrepancy

Understand

Much of machine learning relies on comparing distributions with discrepancy measures.

• Stein's method creates discrepancy measures between two distributions that require only the unnormalized density of one and samples from the other.
• Stein discrepancies can be combined with kernels to define kernelized Stein discrepancies (KSDs).
• While kernels make Stein discrepancies tractable, they pose several challenges in high dimensions.