Automation of analytic descriptions of empirical distributions

Methodological aspects of the identification of empirical distributions of random signals are discussed, along with approaches and methods for automating the ordering and selection of analytic models for the distributions. The idea and features of the modeleteka (model library) as a new multipurpose means for automating the identification of distributions, measuring various characteristics, and simulation and variational modelling of random signals are described, as well as the relationship of these to intellectual data analysis.