Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовская ординарная логистическая регрессия× | Байесовская обобщенная линейная модель× | |
|---|---|---|
| Область | Статистика | Статистика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1999 | 1989 (GLM); 1995 (Bayesian BDA) |
| Автор метода≠ | Johnson & Albert (1999); Bayesian proportional odds framework | McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al. |
| Тип≠ | Bayesian generalized linear model | Bayesian regression model |
| Основополагающий источник≠ | 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 |
| Другие названия | Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model | Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM |
| Связанные | 6 | 6 |
| Сводка≠ | 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. | A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome. |
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