Decomposition of structural learning about directed acyclic graphs

DOI10.1016/j.artint.2005.12.004zbMath1131.68510OpenAlexW1965315715MaRDI QIDQ2457633

Zhi Geng, Qiang Zhao, Xianchao Xie

Publication date: 23 October 2007

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2005.12.004

zbMATH Keywords

decompositiondirected acyclic graphundirected graphconditional independenceBayesian networkjunction treestructural learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (13)

Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach ⋮ Identifiability of intermediate variables on causal paths ⋮ Discovering causes and effects of a given node in Bayesian networks ⋮ Structural learning about directed acyclic graphs from multiple databases ⋮ Decomposition of Covariate-Dependent Graphical Models with Categorical Data ⋮ Structural learning for Bayesian networks by testing complete separators in prime blocks ⋮ CDSC: causal decomposition based on spectral clustering ⋮ A decomposition algorithm for learning Bayesian networks based on scoring function ⋮ A decomposition-based algorithm for learning the structure of multivariate regression chain graphs ⋮ A new algorithm for decomposition of graphical models ⋮ A note on minimal d-separation trees for structural learning ⋮ Decomposition of two classes of structural models ⋮ Learning causal graphs of nonlinear structural vector autoregressive model using information theory criteria

Uses Software

• TETRAD
• MIM

Cites Work

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• Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs
• Algorithmic Aspects of Vertex Elimination on Graphs
• Probabilistic Networks and Expert Systems
• A sufficiently fast algorithm for finding close to optimal clique trees

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