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	階層ベイズネットワーク	ベイジアンネットワーク
分野	ベイズ	ベイズ
系統	Bayesian methods	Bayesian methods
提唱年	1990s–2000s	1988
提唱者	Koller, Friedman, and colleagues	Judea Pearl
種類	probabilistic graphical model	Probabilistic graphical model
原典	Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. ISBN: 978-0262013192	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
別名	HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model	Bayes network, belief network, probabilistic graphical model, directed graphical model
関連	6	4
概要	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.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.