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Statistical system models provide the basis for the examination of varioussorts of distributions. Classification distributions are a very common andversatile form of statistics in e.g. real economic, social, and IT systems. Thestatistical distributions of classification features can be applied indetermining the a priori probabilities in Bayesian networks. We investigate astatistical model of classification distributions based on finding the criticalpoint of a specialized form of entropy. A distribution function forclassification features is derived, with the two parameters $n_0$, minimalclass, and $\bar{N}$, average number of classes. Efficient algorithms for thecomputation of the class probabilities and the approximation of real frequencydistributions are developed and applied to examples from different domains. Themethod is compared to established distributions like Zipf's law. The majorityof examples can be approximated with a sufficient quality ($3-5\%$).

Causal statistical modeling and calculation of distribution functions of classification features