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| Mạng Bayes Phân cấp× | Mô hình phân cấp Bayes có dữ liệu thiếu× | |
|---|---|---|
| Lĩnh vực | Bayes | Bayes |
| Họ | Bayesian methods | Bayesian methods |
| Năm ra đời | 1990s–2000s | 1990s–2000s |
| Người khởi xướng≠ | Koller, Friedman, and colleagues | Gelman, Rubin, Little (and collaborators) |
| Loại≠ | probabilistic graphical model | Bayesian hierarchical model with missing-data integration |
| Công trình gốc≠ | Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192 | 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 |
| Tên gọi khác | HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model | BHM missing data, multilevel Bayesian missing data model, hierarchical Bayesian imputation, Bayesian multilevel model with incomplete data |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | A hierarchical Bayesian network is a probabilistic graphical model that organizes variables across multiple levels of abstraction. Higher-level nodes govern the prior distributions of lower-level nodes through hyperparameters, enabling structured sharing of information across groups, contexts, or data subsets while preserving the directed acyclic graph (DAG) representation of conditional dependencies. | 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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