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| Bayes-féle Probit modell× | Bayes-féle logisztikus regresszió× | |
|---|---|---|
| Tudományterület≠ | Statisztika | Bayes-statisztika |
| Módszercsalád≠ | Regression model | Bayesian methods |
| Keletkezés éve≠ | 1993 | 2008 |
| Megalkotó≠ | Albert & Chib (data augmentation formulation) | Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008) |
| Típus≠ | Binary regression (Bayesian) | Bayesian classification model |
| Alapmű≠ | Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association, 88(422), 669-679. DOI ↗ | Gelman, A., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383. DOI ↗ |
| Alternatív nevek≠ | Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit | bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon |
| Kapcsolódó≠ | 6 | 3 |
| Összefoglaló≠ | The Bayesian Probit model is a binary regression method that models the probability of a binary outcome using the normal CDF (probit link) within a Bayesian framework. It assigns prior distributions to regression coefficients and updates them with observed data, yielding a full posterior distribution rather than a single point estimate. The Albert-Chib data-augmentation algorithm makes posterior sampling computationally efficient via Gibbs sampling. | Bayesian logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses. |
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