Multilevel Bayesian Network
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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press. · ISBN 978-0262013192
- Getoor, L. & Taskar, B. (Eds.) (2007). Introduction to Statistical Relational Learning. MIT Press. · ISBN 978-0262072885
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.