Sampling Over Riemannian Manifolds Using Kernel Herding
Sandesh Adhikary,Byron Boots,Sandesh Adhikary,Byron Boots
Kernel herding is a deterministic sampling algorithm designed to draw ‘super samples' from probability distributions when provided with their kernel mean embeddings in a reproducing kernel Hilbert space (RKHS). Empirical expectations of functions in the RKHS formed using these super samples tend to converge even faster than random sampling from the true distribution itself. Standard implementation...
