
Kernel Embedding of Distributions - gaussdiditfirst
https://en.wikipedia.org/wiki/Kernel_embedding_of_distributions
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heydenberk
This seems very interesting, but the overview of this article tests my
mathematical knowledge, and I can hardly read the rest without a healthy dose
of intuitive reasoning to infer the unknown terms. As far as I can tell, this
is a method of statistical analysis which does not require assumptions about
the structure of the data in order to perform statistical operations,
including comparing data. Conventional information theoretic methods require
modeling the predictability (or entropy) of data and then performing
statistical operations on the basis of those models, which may be errant,
oversimplified or difficult to determine for complex data sets. Instead, the
data are represented with an arbitrary number of dimensions in a way that
generalizes Euclidean space, and then spatial operations can be performed on
the data.

That's as far as I can understand and I'm afraid that there are mistakes even
in my simplistic summary. Can someone explain it better?

~~~
contingencies
It exceeds my mathematical knowledge, and I gave up trying to read it. My
inference was "use of some kind of transformed space when modelling
distribution" but the benefit/result versus a direct analysis was absolutely a
mystery.

I would still love an explanation of the practical benefits of its use
(preferably without the use of numbers or formulae) and IMHO this is what
Wikipedia should present. If anyone here is are able to write that
description, please do.

