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| Chaîne de Markov Monte Carlo (MCMC)× | Analyse de variance à un facteur× | |
|---|---|---|
| Domaine≠ | Bayésien | Statistique |
| Famille≠ | Bayesian methods | Hypothesis test |
| Année d'origine≠ | — | 1925 |
| Auteur d'origine≠ | — | Ronald A. Fisher |
| Type≠ | Posterior sampling algorithm | Parametric mean comparison |
| Source fondatrice≠ | 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 | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ |
| Alias≠ | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA |
| Apparentées≠ | 3 | 4 |
| Résumé≠ | 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. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. |
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