David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
The introduction of statistical models represented by directed acyclic graphs (DAGs) has proved fruitful in the construction of expert systems, in allowing efficient updating algorithms that take advantage of conditional independence relations (Pearl, 1988, Lauritzen et al. 1993), and in inferring causal structure from conditional independence relations (Spirtes and Glymour, 1991, Spirtes, Glymour and Scheines, 1993, Pearl and Verma, 1991, Cooper, 1992). As a framework for representing the combination of causal and statistical hypotheses, DAG models have shed light on a number of issues in statistics ranging from Simpson’s Paradox to experimental design (Spirtes, Glymour and Scheines, 1993). The relations of DAGs with statistical constraints, and the equivalence and distinguishability properties of DAG models, are now well understood, and their characterization and computation involves three properties connecting graphical structure and probability distributions: (i) a local directed Markov property, (ii) a global directed Markov property, (iii) and factorizations of joint densities according to the structure of a graph (Lauritizen, et al., 1990).
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
|Through your library||Only published papers are available at libraries|
Similar books and articles
Peter Spirtes, Conditional Independence in Directed Cyclical Graphical Models Representing Feedback or Mixtures.
Peter Spirtes, Clark Glymour & Richard Scheines, Automated Search for Causal Relations - Theory and Practice.
Peter Spirtes, Thomas Richardson, Christopher Meek, Richard Scheines & Clark Glymour, Using D-Separation to Calculate Zero Partial Correlations in Linear Models with Correlated Errors.
Peter Spirtes, A Polynomial Time Algorithm for Determining Dag Equivalence in the Presence of Latent Variables and Selection Bias.
Richard Scheines, Clark Glymour & Peter Spirtes, Learning the Structure of Linear Latent Variable Models.
Added to index2009-01-28
Total downloads4 ( #195,393 of 1,004,651 )
Recent downloads (6 months)1 ( #64,617 of 1,004,651 )
How can I increase my downloads?