A geometric view on learning Bayesian network structures

DOI10.1016/j.ijar.2010.01.014zbMath1205.68319OpenAlexW2115395277MaRDI QIDQ985149

Jiří Vomlel, Raymond Hemmecke, Milan Studený

Publication date: 20 July 2010

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2010.01.014

zbMATH Keywords

standard imsetgeometric neighborhoodGES algorithminclusion neighborhoodlearning Bayesian networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (12)

Polyhedral aspects of score equivalence in Bayesian network structure learning ⋮ Exact estimation of multiple directed acyclic graphs ⋮ Characteristic imsets for learning Bayesian network structure ⋮ The Characteristic Imset Polytope of Bayesian Networks with Ordered Nodes ⋮ Generalized Permutohedra from Probabilistic Graphical Models ⋮ Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes ⋮ Learning Bayesian network structure: towards the essential graph by integer linear programming tools ⋮ Polyhedral approaches to learning Bayesian networks ⋮ On open questions in the geometric approach to structural learning Bayesian nets ⋮ Decomposition of two classes of structural models ⋮ Standard imsets for undirected and chain graphical models ⋮ The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture

Uses Software

• cdd
• CPLEX
• 4ti2
• Convex

Cites Work

• A reconstruction algorithm for the essential graph
• Estimating the dimension of a model
• A characterization of Markov equivalence classes for acyclic digraphs
• Lectures on Polytopes
• 10.1162/153244303321897717
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