  If moments of singular measures are passed as inputs to the entropy
maximization procedure, the optimization algorithm might not terminate. The
framework developed in our previous paper demonstrated how input moments of
measures, on a broad range of domains, can be conditioned to ensure convergence
of the entropy maximization. Here we numerically illustrate the developed
framework on simplest possible examples: measures with one-dimensional, bounded
supports. Three examples of measures are used to numerically compare
approximations obtained through entropy maximization with and without the
conditioning step.
