Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Независимый t-критерий для двух выборок× | Метод Монте-Карло по цепям Маркова (MCMC)× | |
|---|---|---|
| Область≠ | Статистика | Байесовские методы |
| Семейство≠ | Hypothesis test | Bayesian methods |
| Год появления≠ | 1908 | — |
| Автор метода≠ | Student (W. S. Gosset) | — |
| Тип≠ | Parametric mean comparison | Posterior sampling algorithm |
| Основополагающий источник≠ | Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25. 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 |
| Другие названия≠ | student t-test, two-sample t-test, unpaired t-test, bağımsız örneklem t-testi | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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