Every LWF and AMP Chain Graph Originates from a Set of Causal Models

DOI10.1007/978-3-319-20807-7_29zbMath1465.62114arXiv1312.2967OpenAlexW2197439582MaRDI QIDQ3451190

José-Maria Peña

Publication date: 10 November 2015

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

Full work available at URL: https://arxiv.org/abs/1312.2967

Mathematics Subject Classification ID

Probabilistic graphical models (62H22)

Related Items (2)

Gated Bayesian networks for algorithmic trading ⋮ AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms

Cites Work

• Temporal aggregation in chain graph models
• Discrete chain graph models
• Faithfulness in chain graphs: the discrete case
• An application of formal argumentation: fusing Bayesian networks in multi-agent systems
• Graphical models for associations between variables, some of which are qualitative and some quantitative
• Lehrer and the consensus proposal
• Linear dependencies represented by chain graphs. With comments and a rejoinder by the authors
• On chain graph models for description of conditional independence structures
• Separation and completeness properties for AMP chain graph Markov models.
• Ancestral graph Markov models.
• Democraticop: a democratic way of aggregating Bayesian network parameters
• Marginal AMP chain graphs
• Markov properties for mixed graphs
• Alternative Markov Properties for Chain Graphs
• Qualitative combination of Bayesian networks
• Chain Graph Models and their Causal Interpretations
• Chain Graph Interpretations and Their Relations
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