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| Statistiques descriptives bayésiennes× | Test du Chi-carré Bayésien× | |
|---|---|---|
| Domaine | Statistique | Statistique |
| Famille | Hypothesis test | Hypothesis test |
| Année d'origine≠ | 1763/1812 | 1967 |
| Auteur d'origine≠ | Thomas Bayes / Pierre-Simon Laplace | I. J. Good; extended by Gunel, Dickey, and Wagenmakers et al. |
| Type≠ | Bayesian parameter estimation | Bayesian nonparametric association test |
| 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 | Good, I. J. (1967). A Bayesian significance test for multinomial distributions. Journal of the Royal Statistical Society: Series B (Methodological), 29(3), 399–418. DOI ↗ |
| Alias | Bayesian summaries, posterior descriptives, Bayesian parameter estimation, credible-interval summaries | Bayesian contingency table test, Bayes factor chi-square, Bayesian goodness-of-fit test, Bayesian association test |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | Bayesian descriptive statistics summarizes data by combining observed information with prior knowledge through Bayes' theorem, yielding posterior distributions over parameters such as the mean and variance. Instead of point estimates and p-values, results are expressed as posterior means, medians, and credible intervals that carry a direct probability interpretation. | The Bayesian chi-square test evaluates independence or goodness-of-fit in frequency tables using Bayes factors rather than classical p-values. It quantifies evidence for or against an association between categorical variables, updating prior beliefs with observed counts and delivering an odds-like ratio that distinguishes 'no evidence' from 'evidence of no effect'. |
| ScholarGateJeu de données ↗ |
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