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| 다수준 베이즈 네트워크× | 다층 베이즈 추론× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1990s–2000s | 1980s–2000s |
| 창시자≠ | Extension of Pearl's Bayesian networks; multilevel formulation developed in statistical relational learning community, 1990s–2000s | Gelman, Hill, Raudenbush, Bryk |
| 유형≠ | Probabilistic graphical model (hierarchical) | Bayesian hierarchical model |
| 원전≠ | Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192 | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| 별칭 | multi-level Bayesian network, hierarchical Bayesian network, MLBN, multilevel probabilistic graphical model | Bayesian multilevel model, Bayesian hierarchical model, Bayesian mixed-effects model, Bayesian random-effects model |
| 관련 | 6 | 6 |
| 요약≠ | A multilevel Bayesian network extends the standard Bayesian network to data with hierarchical or grouped structure — students within schools, patients within hospitals, observations within subjects — by placing separate but linked graphical models at each level, with higher-level parameters governing the conditional probability tables of lower-level nodes. The result is a principled probabilistic framework that captures both within-group relationships and between-group variation. | Multilevel Bayesian inference combines Bayesian probability with hierarchical data structures, treating group-level parameters as drawn from a common population distribution. It simultaneously estimates unit-level effects and the hyperparameters governing their variation, propagating full uncertainty through every level of the hierarchy via posterior sampling. |
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