Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Logística Ordinal Bayesiana× | Regressão Logística Multinomial Bayesiana× | |
|---|---|---|
| Área | Estatística | Estatística |
| Família | Regression model | Regression model |
| Ano de origem≠ | 1999 | 1966 (classical); Bayesian extensions established by 1990s |
| Autor original≠ | Johnson & Albert (1999); Bayesian proportional odds framework | Gelman et al. (Bayesian treatment); classical multinomial logit by Cox (1966) |
| Tipo≠ | Bayesian generalized linear model | Bayesian classification model |
| Fonte seminal≠ | Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484 | 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 |
| Outros nomes | Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model | Bayesian polytomous logistic regression, Bayesian multinomial logit, Bayesian softmax regression, Bayesian nominal logistic regression |
| Relacionados≠ | 6 | 5 |
| Resumo≠ | Bayesian ordinal logistic regression extends the classical proportional odds model by placing prior distributions on the regression coefficients and threshold parameters and updating them with observed data via Bayes' theorem. The result is a full posterior distribution over all parameters, enabling uncertainty quantification without relying on large-sample approximations. | Bayesian Multinomial Logistic Regression models a nominal outcome with three or more unordered categories by placing prior distributions over the regression coefficients and updating them with data via Bayes' theorem. The result is a full posterior distribution over category probabilities for each observation, enabling principled uncertainty quantification and regularization through the prior. |
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