Linganisha mbinu

	Mtandao wa Bayesian wa Ngazi Nyingi	Mtandao wa Bayesian
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1990s–2000s	1988
Mwanzilishi	Extension of Pearl's Bayesian networks; multilevel formulation developed in statistical relational learning community, 1990s–2000s	Judea Pearl
Aina	Probabilistic graphical model (hierarchical)	Probabilistic graphical model
Chanzo asilia	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
Majina mbadala	multi-level Bayesian network, hierarchical Bayesian network, MLBN, multilevel probabilistic graphical model	Bayes network, belief network, probabilistic graphical model, directed graphical model
Zinazohusiana	6	4
Muhtasari	A multilevel Bayesian network extends the standard Bayesian network to data with hierarchical or grouped structure — students within schools, patients within hospitals, observations within subjects — by placing separate but linked graphical models at each level, with higher-level parameters governing the conditional probability tables of lower-level nodes. The result is a principled probabilistic framework that captures both within-group relationships and between-group variation.	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.