2005Unpublished venueRequires access

Notes on the Negative Binomial distribution for word occurrences

Edoardo M. Airoldi

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Abstract

rms of number of words; and #, which is the length of a document expressed as a pure number, multiple of the word-length of the reference text, e.g., # = 1.67 for a text 1670 word long if the reference text is a thousand words, i.e., # = 1000. This allows us to express the rate # as the rate of occurrence of a word in a text of length equal to that of the reference text, , conditionally on the desired, or observed, length of the text, #, expressed as a multiple of the word-length of the reference text. References

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rms of number of words; and #, which is the length of a document expressed as a pure number, multiple of the word-length of the reference text, e.g., # = 1.67 for a text 1670 word long if the reference text is a thousand words, i.e., # = 1000. This allows us to express the rate # as the rate of occurrence of a word in a text of length equal to that of the reference text, , conditionally on the desired, or observed, length of the text, #, expressed as a multiple of the word-length of the reference text. References

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Available abstract

rms of number of words; and #, which is the length of a document expressed as a pure number, multiple of the word-length of the reference text, e.g., # = 1.67 for a text 1670 word long if the reference text is a thousand words, i.e., # = 1000. This allows us to express the rate # as the rate of occurrence of a word in a text of length equal to that of the reference text, , conditionally on the desired, or observed, length of the text, #, expressed as a multiple of the word-length of the reference text. References

Key concepts: Negative binomial distribution, Poisson distribution, Negative multinomial distribution, Mathematics, Poisson binomial distribution, Count data, Binomial distribution, Statistics

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