Vertaile menetelmiä
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| Hierarkkinen Bayesilainen verkko× | Hierarkkinen Bayesilainen päättely× | |
|---|---|---|
| Tieteenala | Bayesilainen tilastotiede | Bayesilainen tilastotiede |
| Menetelmäperhe | Bayesian methods | Bayesian methods |
| Syntyvuosi≠ | 1990s–2000s | 2016 |
| Kehittäjä≠ | Koller, Friedman, and colleagues | Ranganath, Altosaar, Tran & Blei |
| Tyyppi≠ | probabilistic graphical model | Bayesian approximate inference |
| Alkuperäislähde≠ | 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 ↗ |
| Rinnakkaisnimet | HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model | HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | 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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