Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Байєсівська ієрархічна модель з пропущеними даними× | MCMC з пропущеними даними× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 1990s–2000s | 1987 |
| Автор методу≠ | Gelman, Rubin, Little (and collaborators) | Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin |
| Тип≠ | Bayesian hierarchical model with missing-data integration | Bayesian computational method |
| Основоположне джерело≠ | 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 | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860 |
| Інші назви | BHM missing data, multilevel Bayesian missing data model, hierarchical Bayesian imputation, Bayesian multilevel model with incomplete data | MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation |
| Пов'язані≠ | 5 | 6 |
| Підсумок≠ | A Bayesian hierarchical model with missing data treats unobserved values as additional unknowns and samples them jointly with all model parameters from the posterior. The nested structure of the hierarchy borrows strength across groups, while the Bayesian framework naturally propagates uncertainty from missingness through every estimate and prediction. | MCMC with missing data is a Bayesian computational strategy that treats unobserved values as additional unknown parameters. By alternating between sampling the missing values from their predictive distribution and sampling the model parameters from their posterior, the algorithm produces a valid joint posterior that fully accounts for uncertainty introduced by the missingness. |
| ScholarGateНабір даних ↗ |
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