Usporedite metode

	Hijerarhijska Bayesova mreža	Bayesov hijerarhijski model s podacima koji nedostaju
Područje	Bayesovska statistika	Bayesovska statistika
Obitelj	Bayesian methods	Bayesian methods
Godina nastanka	1990s–2000s	1990s–2000s
Tvorac	Koller, Friedman, and colleagues	Gelman, Rubin, Little (and collaborators)
Vrsta	probabilistic graphical model	Bayesian hierarchical model with missing-data integration
Temeljni izvor	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
Drugi nazivi	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
Srodne	6	5
Sažetak	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.