Methoden vergelijken

	Bayesiaanse Lineaire Regressie	Bayesiaanse ANOVA
Vakgebied	Bayesiaanse statistiek	Bayesiaanse statistiek
Familie	Bayesian methods	Bayesian methods
Jaar van ontstaan	2013 (modern reference); foundations 18th–19th century	2012
Grondlegger	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	Rouder, Morey, Speckman & Province
Type	Bayesian linear model	Bayesian hypothesis test / group comparison
Oorspronkelijke bron	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	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.
Aliassen	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması
Verwant	4	4
Samenvatting	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.	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.