Computational Complexity Theory
Zipf's Law is a statistical principle that suggests that in many natural languages and datasets, the frequency of any word is inversely proportional to its rank in the frequency table. This means that the most common word will occur twice as often as the second most common word, three times as often as the third, and so on. It illustrates how certain distributions can be expected to occur in various types of data, which is relevant when analyzing average-case complexity and distributional problems.
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