In this work we consider a probabilistic methodology that models the intensity distributions found in pure and partial volume (PV) voxels. We introduce some methodological developments that enable explicit modeling of the PV voxels prior probability density function (PDF). This new formulation can be applied generically across different imaging modalities including PET and SPECT. In this paper, we establish for the first time, that the prior PDF of voxels that arise from the PV effect in volumetric data can be well described by a simple phenomenological law called Benford's Law, which significantly eases parameter estimation compared to other methods. Results from simulated data are presented, along with a preliminary PET phantom study utilizing registered CT data to determine the quality of the resulting probabilistic voxel classification scheme.