Conditional independence structure and its closure: inferential rules and algorithms

DOI10.1016/j.ijar.2009.05.002zbMath1185.62009OpenAlexW2102123707MaRDI QIDQ962920

Barbara Vantaggi, Giuseppe Busanello, Marco Baioletti

Publication date: 7 April 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.2009.05.002

zbMATH Keywords

closuregraphoid propertiesconditional independence modelsinferential rulesgeneralized inclusion

Mathematics Subject Classification ID

Measures of association (correlation, canonical correlation, etc.) (62H20) Foundations and philosophical topics in statistics (62A01)

Related Items (16)

DAG representation of asymmetric independence models arising in coherent conditional possibility theory ⋮ An Algorithm to Find a Perfect Map for Graphoid Structures ⋮ Concise representations and construction algorithms for semi-graphoid independency models ⋮ Computing Concise Representations of Semi-graphoid Independency Models ⋮ A computational algebraic-geometry method for conditional-independence inference ⋮ Representing independence models with elementary triplets ⋮ Acyclic directed graphs representing independence models ⋮ Consequences of the minimum specificity principle on conditioning and on independence in possibility theory ⋮ A lattice-based representation of independence relations for efficient closure computation ⋮ Finding P–Maps and I–Maps to Represent Conditional Independencies ⋮ Exploiting independencies to compute semigraphoid and graphoid structures ⋮ Compositional models and conditional independence in evidence theory ⋮ Logical and algorithmic properties of stable conditional independence ⋮ Acyclic Directed Graphs to Represent Conditional Independence Models ⋮ Independence for full conditional probabilities: structure, factorization, non-uniqueness, and Bayesian networks ⋮ Asymmetric decomposability and persegram representation in coherent conditional probability theory

Uses Software

• Separoids

Cites Work

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• Graphoid properties of epistemic irrelevance and independence
• Bayesian network learning algorithms using structural restrictions
• Independence concepts in possibility theory. I
• Conditional independence in valuation-based systems
• Semigraphoids and structures of probabilistic conditional independence
• Stochastic independence in a coherent setting
• Strong conditional independence for credal sets
• The L-separation criterion for description of cs-independence models
• On chain graph models for description of conditional independence structures
• Conditional independence in a coherent finite setting
• Separoids: a mathematical framework for conditional independence and irrelevance
• Possibility theory: conditional independence
• Operations for inference in continuous Bayesian networks with linear deterministic variables
• Learning Bayesian network parameters under order constraints
• The role of coherence for handling probabilistic evaluations and independence
• CONDITIONAL INDEPENDENCE RELATIONS IN POSSIBILITY THEORY
• Probabilistic Networks and Expert Systems
• GRAPHOID PROPERTIES OF QUALITATIVE POSSIBILISTIC INDEPENDENCE RELATIONS
• CONDITIONAL INDEPENDENCE STRUCTURES AND GRAPHICAL MODELS
• Symbolic and Quantitative Approaches to Reasoning with Uncertainty
• The role of coherence in eliciting and handling imprecise probabilities and its application to medical diagnosis

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