Resumen
We consider a rather general problem of nonparametric estimation of an uncountable set of probability density functions (p.d.f.?s) of the form: ??(??;??)
f
(
x
;
r
)
, where r is a non-random real variable and ranges from ??1
R
1
to ??2
R
2
. We put emphasis on the algorithmic aspects of this problem, since they are crucial for exploratory analysis of big data that are needed for the estimation. A specialized learning algorithm, based on the 2D FFT, is proposed and tested on observations that allow for estimate p.d.f.?s of a jet engine temperatures as a function of its rotation speed. We also derive theoretical results concerning the convergence of the estimation procedure that contains hints on selecting parameters of the estimation algorithm.