विधियों की तुलना करें

	बेयसियन एनोवा	बेयसियन रिग्रेशन	साधारण न्यूनतम वर्ग (OLS) समाश्रयण
क्षेत्र	बायेसियन	बायेसियन	अर्थमिति
परिवार	Bayesian methods	Bayesian methods	Regression model
उद्भव वर्ष	2012	—	2019
प्रवर्तक	Rouder, Morey, Speckman & Province	—	Wooldridge (textbook treatment); classical least squares
प्रकार	Bayesian hypothesis test / group comparison	Bayesian linear model	Linear regression
मौलिक स्रोत	Rouder, J. N., Morey, R. D., Speckman, P. L. & Province, J. M. (2012). Default Bayes Factors for ANOVA Designs. Journal of Mathematical Psychology, 56(5), 356–374.	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
उपनाम	bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması	bayesian linear regression, probabilistic regression, bayesian regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
संबंधित	4	2	5
सारांश	Bayesian ANOVA, formalised by Rouder, Morey, Speckman and Province (2012), tests whether group means differ by quantifying the evidence for the alternative hypothesis relative to the null using the Bayes Factor (BF₁₀). Unlike classical ANOVA, it can also measure evidence in favour of the null hypothesis, making it equally informative when groups do not differ.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).