Salīdzināt metodes

	Hierarchical Bayesian Network	Hierarchical Variational Inference
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1990s–2000s	2016
Autors	Koller, Friedman, and colleagues	Ranganath, Altosaar, Tran & Blei
Tips	probabilistic graphical model	Bayesian approximate inference
Pirmavots	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.
Citi nosaukumi	HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model	HVI, hierarchical variational models, hierarchical VI, hierarchical approximate inference
Saistītās	6	5
Kopsavilkums	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.