Salīdzināt metodes

	Bejeziešu ANKOVA (Bayesian ANCOVA)	Bāziskā lineārā regresija
Nozare	Statistika	Bajesa metodes
Saime	Hypothesis test	Bayesian methods
Izcelsmes gads	2012 (formalized; Bayesian general linear models since 1960s)	2013 (modern reference); foundations 18th–19th century
Autors	Building on Jeffreys (1961) and developed formally for regression/ANCOVA by Rouder & Morey (2012)	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.
Tips	Bayesian parametric covariate-adjusted group comparison	Bayesian linear model
Pirmavots	Rouder, J. N., & Morey, R. D. (2012). Default Bayes factors for model selection in regression. Multivariate Behavioral Research, 47(6), 877–903.	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
Citi nosaukumi	Bayesian ANCOVA, Bayesian analysis of covariance, B-ANCOVA, Bayesian covariate-adjusted group comparison	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon
Saistītās	5	4
Kopsavilkums	Bayesian Analysis of Covariance (Bayesian ANCOVA) extends classical ANCOVA by placing prior distributions on group effects and covariate slopes, then updating them with observed data to obtain posterior distributions and Bayes factors. It quantifies evidence for group differences on a continuous outcome after statistically adjusting for one or more continuous covariates, without relying on p-value thresholds.	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.