Compară metode

	Regresie Liniară Bayesiană	Testul t pentru eșantioane independente	Estimarea prin Verosimilitate Maximă
Domeniu	Bayesian	Statistică	Statistică
Familie	Bayesian methods	Hypothesis test	Regression model
Anul apariției	2013 (modern reference); foundations 18th–19th century	1908	1922
Autorul original	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	Student (W. S. Gosset)	R. A. Fisher
Tip	Bayesian linear model	Parametric mean comparison	Parametric point estimator
Sursa seminală	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.	Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368.
Denumiri alternative	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi	MLE, maximum-likelihood estimator, ML estimation, Fisher's method of maximum likelihood
Înrudite	4	4	4
Rezumat	Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.	The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.	Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures.