Optimal estimate of probability density functions from experimental data

Abstract

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.