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| MCMC với dữ liệu thiếu× | Suy luận Bayes với dữ liệu thiếu× | |
|---|---|---|
| Lĩnh vực | Bayes | Bayes |
| Họ | Bayesian methods | Bayesian methods |
| Năm ra đời≠ | 1987 | 1976–1987 |
| Người khởi xướng≠ | Tanner & Wong (data augmentation); extended by Gelfand & Smith, Rubin | Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation) |
| Loại≠ | Bayesian computational method | Bayesian probabilistic model |
| Công trình gốc≠ | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860 | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860 |
| Tên gọi khác | MCMC missing data, data augmentation MCMC, Bayesian multiple imputation, MCMC imputation | Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | 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. | 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. |
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