  We report on a new framework to parametrize parton distribution functions
(PDFs) and other hadronic nonperturbative functions using polynomial functions
realized by B\'ezier curves. B\'ezier parameterizations produce a stable fit
with a low number of free parameters, while competing in performance with
neural networks and offering explicit interpretation. We specifically apply
this approach to determine PDFs in a pion, essential for understanding of
nonperturbative QCD dynamics.
