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حوزهبیزیبیزی
خانوادهBayesian methodsBayesian methods
سال پیدایش1990s–2000s1990s–2000s
پدیدآورKoller, Friedman, and colleaguesGelman, Rubin, Little (and collaborators)
نوعprobabilistic graphical modelBayesian hierarchical model with missing-data integration
منبع بنیادینKoller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192Gelman, 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
نام‌های دیگرHBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical modelBHM missing data, multilevel Bayesian missing data model, hierarchical Bayesian imputation, Bayesian multilevel model with incomplete data
مرتبط65
خلاصهA 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.
ScholarGateمجموعه‌داده
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  3. PUBLISHED
  1. v1
  2. 2 منابع
  3. PUBLISHED

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ScholarGateمقایسهٔ روش‌ها: Hierarchical Bayesian Network · Bayesian Hierarchical Model with Missing Data. بازیابی‌شده در 2026-06-17 از https://scholargate.app/fa/compare