  Taking advantage of the fact that the cardinalities of hidden variables in
network scenarios can be assumed to be finite without loss of generality, a
numerical tool for finding explicit local models that reproduce a given
statistical behaviour was developed. The numerical procedure was then validated
using families of statistical behaviours for which the network-local boundary
is known, in the bilocal scenario. Furthermore, the critical visibility for 3
notable distributions mixed with a uniform random noise is investigated in the
triangle network without inputs. We provide conjectures for the critical
visibilities of the Greenberger-Horne-Zeilinger (GHZ) and W distributions
(which are roots of 4th degree polynomials), as well as a lower bound estimate
of the critical visibility of the Elegant Joint Measurement distribution. The
developed codes and documentation are publicly available at
github.com/mariofilho281/localmodels
