Sammenlign metoder

	Forfatterskabsattribuering (Stylometri)	Bayesiansk inferens
Fagområde	Tekstmining	Statistik
Familie	Machine learning	Bayesian methods
Oprindelsesår	2009	1763
Ophavsperson	Mosteller & Wallace; Stamatatos	Thomas Bayes; Pierre-Simon Laplace
Type	Supervised stylometric classification	Probabilistic inference paradigm
Oprindelig kilde	Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Aliasser	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Relaterede	3	3
Resumé	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.	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.