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Comparison of mixture-based classification with the data-dependent pointer model for various types of components
Likhonina, Raissa ; Suzdaleva, Evgenia ; Nagy, Ivan
The presented report is devoted to the analysis of a data-dependent pointer model, whether it brings some advantages in comparison with a data-independent pointer model at simulation and estimation of components referring to different types of distribution, including categorical, uniform, exponential and state-space components for a dynamic data-dependent model, and normal components for a static data-dependent pointer model.

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