Linganisha mbinu

	Uchambuzi wa Uandishi (Stylometry)	Uchanganuzi wa Kiwango cha Uwezekano wa Uhalifu (LR)
Nyanja	Uchimbaji wa Matini	Sayansi ya Forensiki
Familia	Machine learning	Regression model
Mwaka wa asili	2009	2004
Mwanzilishi	Mosteller & Wallace; Stamatatos	Colin Aitken & Franco Taroni
Aina	Supervised stylometric classification	Bayesian evidence evaluation model
Chanzo asilia	Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.	Aitken, C. G. G., & Taroni, F. (2004). Statistics and the Evaluation of Evidence for Forensic Scientists (2nd ed.). Wiley. ISBN: 978-0-470-84367-3
Majina mbadala	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı
Zinazohusiana	3	3
Muhtasari	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 Forensic Likelihood Ratio (LR) is a Bayesian framework for quantifying the weight of forensic evidence relative to two competing propositions — typically the prosecution and defence hypotheses. Formally developed and systematised by Colin Aitken and Franco Taroni in their 2004 Wiley monograph, the LR expresses how much more probable the observed evidence is under one hypothesis than under the other, providing the court with a single, interpretable number that separates the scientist's role from the fact-finder's role.