Probabilistic modeling and visualization of crater shape-maturity relationships is explored in context of remote sensing data acquired by Apollo, Clementine and Lunar Reconnaissance Orbiter spacecraft. Unlike any earlier attempt of understanding relationships between lunar crater features (depth and diameter), relative age of crater formation (Pre-Nectarian to Copernican) and optical maturity of the lunar surface (OMAT values), the joint probability of these variables is modeled. The proposed model is strongly dependent on data density and is not based on deterministic equations as in earlier works. Once developed, a joint probability model can accommodate additional factors through conditional probability weights in a Bayesian network architecture. It is expected that probabilistic modeling will facilitate visualization of relationships between experimental variables and eventually help gain additional insight into lunar cratering mechanisms and linkages between crater morphology, spectral properties and crater degradation mechanisms. The described simple Bayesian network in this work is by no means complete, but illustrates the potential of the proposed novel method with the advent of high resolution images and topographic measurements for the Moon.