  Setting initial values of parameters of mixture distributions estimated by
using the EM recursive algorithm is very important to the overall quality of
estimation. None of the existing methods is suitable for mixtures with large
number of components. We present a relevant methodology of estimating initial
values of parameters of univariate, heteroscedastic Gaussian mixtures, on the
basis of the dynamic programming algorithm for partitioning the range of
observations into bins. We evaluate variants of dynamic programming method
corresponding to different scoring functions for partitioning. For simulated
and real datasets we demonstrate superior efficiency of the proposed method
compared to existing techniques.
