เปรียบเทียบวิธี

	อัตราส่วนความน่าจะเป็นทางนิติวิทยาศาสตร์	การระบุผู้ประพันธ์ (สไตโลเมทรี)	การทดสอบด้วยแฟกเตอร์เบย์ส
สาขาวิชา	นิติวิทยาศาสตร์	การทำเหมืองข้อความ	เบย์
ตระกูล	Regression model	Machine learning	Bayesian methods
ปีกำเนิด	2004	2009	1961
ผู้ริเริ่ม	Colin Aitken & Franco Taroni	Mosteller & Wallace; Stamatatos	Harold Jeffreys
ประเภท	Bayesian evidence evaluation model	Supervised stylometric classification	Bayesian hypothesis comparison
แหล่งต้นตำรับ	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	Stamatatos, E. (2009). A survey of modern authorship attribution methods. Journal of the American Society for Information Science and Technology, 60(3), 538–556.	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682
ชื่อเรียกอื่น	Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification	bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi
ที่เกี่ยวข้อง	3	3	3
สรุป	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.	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.