In this paper, we study three recently proposed probability mass function (PMF) estimation methods for flow cytometry data analysis. By modeling the PMFs as a mixture of simpler distributions, we can reformulate the PMF estimation problem as three different tensor-based approaches: a least squares coupled tensor factorization approach, a least squares partially coupled tensor factorization approach, and a Kullback-Leibler divergence (KLD)-based expectation-maximization (EM) approach. In the coupled methods, the full PMF is estimated from lower-order empirical marginal distributions, while the EM approach estimates the full PMF directly from the observed data. The three approaches are evaluated in the context of simulated and real data experiments.