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| Bayesovská ANOVA× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|
| Odbor | Bayesovské metódy | Bayesovské metódy |
| Rodina | Bayesian methods | Bayesian methods |
| Rok vzniku≠ | 2012 | — |
| Tvorca≠ | Rouder, Morey, Speckman & Province | — |
| Typ≠ | Bayesian hypothesis test / group comparison | Posterior sampling algorithm |
| Pôvodný zdroj≠ | 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. DOI ↗ | 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 |
| Ďalšie názvy≠ | bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Príbuzné≠ | 4 | 3 |
| Zhrnutie≠ | 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. | 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. |
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