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| Тест с Байесов фактор× | Байесов регресионен модел× | t-тест за независими извадки× | |
|---|---|---|---|
| Област≠ | Бейсови методи | Бейсови методи | Статистика |
| Семейство≠ | Bayesian methods | Bayesian methods | Hypothesis test |
| Година на възникване≠ | 1961 | — | 1908 |
| Създател≠ | Harold Jeffreys | — | Student (W. S. Gosset) |
| Тип≠ | Bayesian hypothesis comparison | Bayesian linear model | Parametric mean comparison |
| Основополагащ източник≠ | 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 | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. DOI ↗ |
| Други названия≠ | bayes factor, BF10, Bayesian hypothesis test, Bayes Faktörü — Hipotez Testi | bayesian linear regression, probabilistic regression, bayesian regresyon | student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi |
| Свързани≠ | 3 | 2 | 4 |
| Резюме≠ | 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. | The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances. |
| ScholarGateНабор от данни ↗ |
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