方法对比
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| 层级贝叶斯网络× | 分层变分推断× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 1990s–2000s | 2016 |
| 提出者≠ | Koller, Friedman, and colleagues | Ranganath, Altosaar, Tran & Blei |
| 类型≠ | probabilistic graphical model | Bayesian approximate inference |
| 开创性文献≠ | Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192 | Ranganath, R., Altosaar, J., Tran, D. & Blei, D. M. (2016). Hierarchical Variational Models. Proceedings of the 33rd International Conference on Machine Learning (ICML 2016), PMLR 48, 324-333. link ↗ |
| 别名 | HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model | HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference |
| 相关≠ | 6 | 5 |
| 摘要≠ | 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. | Hierarchical variational inference (HVI) extends standard variational inference by placing a richer, hierarchical structure on the variational family itself. Instead of using a simple mean-field approximation, HVI introduces auxiliary latent variables that capture dependencies among the main latent variables, yielding tighter evidence lower bounds and more accurate posterior approximations for complex Bayesian models. |
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