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| 다수준 베이즈 네트워크× | 동적 베이즈 네트워크× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 1990s–2000s | 1989 |
| 창시자≠ | Extension of Pearl's Bayesian networks; multilevel formulation developed in statistical relational learning community, 1990s–2000s | Thomas Dean & Keiji Kanazawa |
| 유형≠ | Probabilistic graphical model (hierarchical) | probabilistic graphical model for sequences |
| 원전≠ | Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192 | Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI ↗ |
| 별칭 | multi-level Bayesian network, hierarchical Bayesian network, MLBN, multilevel probabilistic graphical model | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network |
| 관련≠ | 6 | 5 |
| 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. | A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty. |
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