Võrdle meetodeid

	Kohtuekspertiisi tõenäosussuhe (LR)	Bayes Factor Test	Bayesi järeldamine
Valdkond	Kohtuekspertiis	Bayesi meetodid	Statistika
Perekond	Regression model	Bayesian methods	Bayesian methods
Tekkeaasta	2004	1961	1763
Looja	Colin Aitken & Franco Taroni	Harold Jeffreys	Thomas Bayes; Pierre-Simon Laplace
Tüüp	Bayesian evidence evaluation model	Bayesian hypothesis comparison	Probabilistic inference paradigm
Algallikas	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	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682	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.
Rööpnimetused	Bayes Factor in Forensics, Forensic Evidence Weight, LR-Based Forensic Evaluation, Adli Olabilirlik Oranı	bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Seotud	3	3	3
Kokkuvõte	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.	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.	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.