Võrdle meetodeid

	Bayesi ANOVA	Bayes Factor Test	Markovi ahel-Monte Carlo (MCMC)
Valdkond	Bayesi meetodid	Bayesi meetodid	Bayesi meetodid
Perekond	Bayesian methods	Bayesian methods	Bayesian methods
Tekkeaasta	2012	1961	—
Looja	Rouder, Morey, Speckman & Province	Harold Jeffreys	—
Tüüp	Bayesian hypothesis test / group comparison	Bayesian hypothesis comparison	Posterior sampling algorithm
Algallikas	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.	Jeffreys, H. (1961). Theory of Probability (3rd ed.). Clarendon Press / Oxford University Press. ISBN: 978-0198503682	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
Rööpnimetused	bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması	bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
Seotud	4	3	3
Kokkuvõte	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.	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.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.