Comparer des méthodes

	Réseau bayésien hiérarchique	Réseau bayésien dynamique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1990s–2000s	1989
Auteur d'origine	Koller, Friedman, and colleagues	Thomas Dean & Keiji Kanazawa
Type	probabilistic graphical model	probabilistic graphical model for sequences
Source fondatrice	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.
Alias	HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
Apparentées	6	5
Résumé	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.	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.