Nonparametric density estimation on Riemannian manifolds extends classical techniques to data that lie on curved spaces rather than in Euclidean domains. Such manifolds may arise as spheres, rotation ...
Kernel density estimation (KDE) is a cornerstone of non-parametric statistics, offering a flexible means to infer an underlying probability density from finite samples without assuming a predetermined ...
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