방법 비교

	계층적 베이즈 네트워크	베이즈 네트워크
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1990s–2000s	1988
창시자	Koller, Friedman, and colleagues	Judea Pearl
유형	probabilistic graphical model	Probabilistic graphical model
원전	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
별칭	HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model	Bayes network, belief network, probabilistic graphical model, directed graphical model
관련	6	4
요약	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.