Conditional independence structures and graphical models
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Logical and algorithmic properties of stable conditional independence
International Journal of Approximate Reasoning
A geometric view on learning Bayesian network structures
International Journal of Approximate Reasoning
On open questions in the geometric approach to structural learning Bayesian nets
International Journal of Approximate Reasoning
Characteristic imsets for learning Bayesian network structure
International Journal of Approximate Reasoning
Reading dependencies from covariance graphs
International Journal of Approximate Reasoning
Compositional models in valuation-based systems
International Journal of Approximate Reasoning
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Probabilistic Conditional Independence Structures provides the mathematical description of probabilistic conditional independence structures; the author uses non-graphical methods of their description, and takes an algebraic approach. The monograph presents the methods of structural imsets and supermodular functions, and deals with independence implication and equivalence of structural imsets. Motivation, mathematical foundations and areas of application are included, and a rough overview of graphical methods is also given. In particular, the author has been careful to use suitable terminology, and presents the work so that it will be understood by both statisticians, and by researchers in artificial intelligence. The necessary elementary mathematical notions are recalled in an appendix.