পদ্ধতির তুলনা করুন

	ফোরেনসিক লাইকলিহুড রেশিও (LR)	লেখকত্ব আরোপ (স্টাইলোমেট্রি)
ক্ষেত্র	ফরেনসিক বিজ্ঞান	টেক্সট খনন
পরিবার	Regression model	Machine learning
উদ্ভবের বছর	2004	2009
প্রবর্তক	Colin Aitken & Franco Taroni	Mosteller & Wallace; Stamatatos
ধরন	Bayesian evidence evaluation model	Supervised stylometric classification
মৌলিক উৎস	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.
অপর নাম	Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı	Stylometry, Authorship Analysis, Yazarlık Atıfı, Authorship Identification
সম্পর্কিত	3	3
সারসংক্ষেপ	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.