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| Autorschaftszuschreibung (Stilometrie)× | Bayes-Faktor-Test× | |
|---|---|---|
| Fachgebiet≠ | Text Mining | Bayes-Statistik |
| Familie≠ | Machine learning | Bayesian methods |
| Entstehungsjahr≠ | 2009 | 1961 |
| Urheber≠ | Mosteller & Wallace; Stamatatos | Harold Jeffreys |
| Typ≠ | Supervised stylometric classification | Bayesian hypothesis comparison |
| Wegweisende Quelle≠ | Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556. DOI ↗ | Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682 |
| Aliasnamen | Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification | bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | Authorship attribution is the task of identifying the most probable author of an anonymous or disputed text by analysing its stylistic fingerprint. Rooted in the statistical work of Mosteller and Wallace on the Federalist Papers (1964), the field was systematically surveyed and formalised by Stamatatos (2009), who catalogued feature sets ranging from character n-grams and function-word frequencies to syntactic and semantic representations used by modern machine-learning classifiers. | The Bayes factor test, formalised by Harold Jeffreys in 1961, is a Bayesian method for comparing two competing hypotheses. Rather than returning a binary reject/retain verdict, it produces a continuous ratio BF₁₀ that quantifies how much more (or less) probable the data are under the alternative hypothesis H₁ than under the null hypothesis H₀. |
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