Order-Independent Structure Learning of Multivariate Regression Chain Graphs

DOI10.1007/978-3-030-35514-2_24zbMath1440.68217arXiv1910.01067OpenAlexW2996864919MaRDI QIDQ3297816

Mohammad Ali Javidian, Marco Valtorta, Pooyan Jamshidi

Publication date: 20 July 2020

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.01067

zbMATH Keywords

high-dimensional datastructural learningorder independencemultivariate regression chain graphscalable machine learning techniques

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms ⋮ A decomposition-based algorithm for learning the structure of multivariate regression chain graphs

Uses Software

• TETRAD

Cites Work

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• Discrete chain graph models
• The max-min hill-climbing Bayesian network structure learning algorithm
• Graphical models for associations between variables, some of which are qualitative and some quantitative
• Causation, prediction, and search
• Linear dependencies represented by chain graphs. With comments and a rejoinder by the authors
• Sequences of regressions and their independences
• Chain graph interpretations and their relations revisited
• Markov Properties for Acyclic Directed Mixed Graphs

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