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| 베이지안 순서형 로지스틱 회귀분석× | 베이지안 프로빗 모형× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1999 | 1993 |
| 창시자≠ | Johnson & Albert (1999); Bayesian proportional odds framework | Albert & Chib (data augmentation formulation) |
| 유형≠ | Bayesian generalized linear model | Binary regression (Bayesian) |
| 원전≠ | Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484 | 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 ↗ |
| 별칭 | Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model | Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit |
| 관련 | 6 | 6 |
| 요약≠ | 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. | 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. |
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