방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 결측 데이터가 있는 베이즈 추론× | 계층적 베이즈 추론× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1976–1987 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| 창시자≠ | Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation) | Lindley & Smith; Gelman et al. |
| 유형≠ | Bayesian probabilistic model | Bayesian multilevel model |
| 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 | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| 관련 | 6 | 6 |
| 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. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
| ScholarGate데이터셋 ↗ |
|
|