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| 결측 데이터가 있는 베이즈 추론× | 결측값이 있는 베이즈 계층 모델× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1976–1987 | 1990s–2000s |
| 창시자≠ | Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation) | Gelman, Rubin, Little (and collaborators) |
| 유형≠ | Bayesian probabilistic model | Bayesian hierarchical model with missing-data integration |
| 원전≠ | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860 | 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 |
| 별칭 | Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model | BHM missing data, multilevel Bayesian missing data model, hierarchical Bayesian imputation, Bayesian multilevel model with incomplete data |
| 관련≠ | 6 | 5 |
| 요약≠ | Bayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us. | 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. |
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