Conditional independence and dependence properties in factor models. The generalization to graphical models. Directed acyclic graphs. DAG models. Factor, mixture, and Markov models as DAGs. The graphical Markov property. Reading conditional independence properties from a DAG. Creating conditional dependence properties from a DAG. Statistical aspects of DAGs. Reasoning with DAGS; does asbestos whiten teeth? Appendix: undirected graphical models, the Gibbs-Markov theorem; directed but cyclic graphical models. Appendix: Some basic notions of graph theory; Guthrie diagrams.
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