Kolmogorov’s criterion, named after Russian mathematician Andrei Kolmogorov, is a generalization of the maximum likelihood principle that is used in statistics.
It states that for a given data set, the probability of obtaining a sample from the data set that is as close to the desired sample as possible (i.e., maximizes the likelihood) is maximized by assuming that the data set consists of a random sample from some underlying distribution.
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