Dynamic light scattering (DLS) is a promising technique for early cataract detection and for studying cataractogenesis. A novel probabilistic analysis tool, the sparse Bayesian learning (SBL) algorithm, is described for reconstructing the most-probable size distribution of α-crystallin and their aggregates in an ocular lens from the DLS data. The performance of the algorithm is evaluated by analyzing simulated correlation data from known distributions and DLS data from the ocular lenses of a fetal calf, a Rhesus monkey, and a man, so as to establish the required efficiency of the SBL algorithm for clinical studies.