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| Hierarchisches Bayes'sches Netz× | Bayesian Hierarchical Model with Missing Data× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr | 1990s–2000s | 1990s–2000s |
| Urheber≠ | Koller, Friedman, and colleagues | Gelman, Rubin, Little (and collaborators) |
| Typ≠ | probabilistic graphical model | Bayesian hierarchical model with missing-data integration |
| Wegweisende Quelle≠ | 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 |
| Aliasnamen | 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 |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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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