Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beieziešu Probit modelis× | Bajeziāņu ordinālā loģistikas regresija× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1993 | 1999 |
| Autors≠ | Albert & Chib (data augmentation formulation) | Johnson & Albert (1999); Bayesian proportional odds framework |
| Tips≠ | Binary regression (Bayesian) | Bayesian generalized linear model |
| Pirmavots≠ | 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 ↗ | Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. Springer. ISBN: 978-0387987484 |
| Citi nosaukumi | Bayesian probit regression, probit model with data augmentation, Gibbs sampling probit, Albert-Chib probit | Bayesian proportional odds model, Bayesian cumulative logit model, Bayesian ordered logit, Bayesian cumulative link model |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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 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. |
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