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| Байесова ANOVA× | Тест с Байесов фактор× | Байесов регресионен модел× | |
|---|---|---|---|
| Област | Бейсови методи | Бейсови методи | Бейсови методи |
| Семейство | Bayesian methods | Bayesian methods | Bayesian methods |
| Година на възникване≠ | 2012 | 1961 | — |
| Създател≠ | Rouder, Morey, Speckman & Province | Harold Jeffreys | — |
| Тип≠ | Bayesian hypothesis test / group comparison | Bayesian hypothesis comparison | Bayesian linear model |
| Основополагащ източник≠ | 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 ↗ | 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 |
| Други названия≠ | 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 | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Свързани≠ | 4 | 3 | 2 |
| Резюме≠ | 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. | 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. |
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