A new semi-nonparametric method for modeling data with Normal variance-mean mixtures (NVMM) is presented. This new method is based on the least-squares programming routine, UNMIX. Density estimates based on random samples of size n from two, three, and four components of NVMM are found using UNMIX. Graphical comparisons of the UNMIX fit found by the EM Algorithm and the Bayesian approaches are done using three real life examples. A quantitative comparison using the AIC and Chi-Square is done for one of the most commonly used examples, the Galaxy Data. The results are promising and the method has great potential for improvement.