We consider the classical problem of missing-mass estimation, which deals with estimating total probability unseen elements in a sample. The estimation has various applications machine learning, statistics, language processing, ecology, sensor networks, and others. naive, constrained maximum likelihood (CML) estimator is inappropriate for this since it tends to overestimate observed elements. S...