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| Hierarchikus Bayes-hálózat× | Bayes-háló× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 1990s–2000s | 1988 |
| Megalkotó≠ | Koller, Friedman, and colleagues | Judea Pearl |
| Típus | probabilistic graphical model | Probabilistic graphical model |
| Alapmű≠ | 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 |
| Alternatív nevek≠ | HBN, layered Bayesian network, multi-level Bayesian network, hierarchical probabilistic graphical model | Bayes network, belief network, probabilistic graphical model, directed graphical model |
| Kapcsolódó≠ | 6 | 4 |
| Összefoglaló≠ | 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. |
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