  A method providing optimal estimate of probability density functions (PDFs)
from time series is proposed. It allows almost arbitrary resolution PDFs when
applied to either, sampled analytic functions or digitized data from
experiments. When results are compared with PDFs of the same data calculated
using the standard histogram method, a remarkable improvement is observed,
especially in far lateral regions of the PDF, where low probability events give
poor statistics.
