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| 베이지안 다변량 분산 분석 (Bayesian MANOVA)× | 베이지안 일원 분산 분석× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Hypothesis test | Hypothesis test |
| 기원 연도≠ | 1970s–2010s | 1961 (foundations); 2012 (ANOVA Bayes factors) |
| 창시자≠ | Bayesian framework applied to MANOVA; foundational multivariate Bayesian work by Dickey (1974) and Rouder et al. (2012) | Harold Jeffreys (foundations); Jeffrey Rouder et al. (default priors for ANOVA) |
| 유형≠ | Bayesian multivariate group comparison | Bayesian hypothesis test |
| 원전≠ | Olkin, I., & Rubin, H. (1964). Multivariate beta distributions and independence properties of the Wishart distribution. The Annals of Mathematical Statistics, 35(1), 261–269. DOI ↗ | 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 ↗ |
| 별칭 | Bayesian MANOVA, Bayesian multivariate ANOVA, BF-MANOVA, Bayesian multivariate group comparison | Bayesian ANOVA, BF ANOVA, Bayes factor one-way ANOVA, Bayesian F-test |
| 관련≠ | 5 | 3 |
| 요약≠ | Bayesian Multivariate Analysis of Variance (Bayesian MANOVA) extends the classical MANOVA framework by replacing null-hypothesis significance testing with Bayesian inference. It uses prior distributions on multivariate group means and covariance structures, updates them with data to yield posterior distributions, and quantifies evidence through Bayes factors rather than p-values. | Bayesian one-way ANOVA tests whether the means of three or more independent groups differ by computing a Bayes factor — a ratio that quantifies how much more likely the data are under a model that allows group differences than under the null model that assumes equal means. Unlike the classical F-test, it provides direct evidence for or against the null hypothesis rather than merely rejecting or retaining it. |
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