Alternative Markov Properties for Chain Graphs

By Steen A. Andersson, David Madigan, Michael D. Perlman. 2001

178 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2f6deeed7edb6604416f1bd45afdb5760947a63b

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In Artificial Intelligence

In Management Science and Operations Research

In Molecular Biology

Citations in the corpus

Markov properties for mixed graphs (2011, methodology): “These range from undirected graphs with simple separation for derivation of independencies [19] to various forms of mixed graphs [18; 25; 29], including chain graphs with several different separation criteria used for derivation of independencies [10; 5; 17; 2; 8].”
Discrete chain graph models (2009, background): “Block-recursive Markov properties for a chain graph G employ the recursive structure of the chain components captured in the DAG D = D(G); see [1, 21].”
Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, unknown): “All these methods require considerable A u t h o r ' s p e r s o n a l”
Minimal sufficient causation and directed acyclic graphs (2009, background): “Another direction of further research might involve the incorporation of the AND and OR nodes that arise from sufficient causation structures into other graphical models such as summary graphs [4], MC-graphs [12], chain graph models [2, 6, 14-16, 23, 34, 42] and ancestral graph models [24].”
Multimodality of the likelihood in the bivariate seemingly unrelated regressions model (2004, background): “More recently, seemingly unrelated regressions arose in likelihood factorisations of Gaussian graphical models (Andersson et al., 2001; Richardson & Spirtes, 2002). Bayesian approaches are reviewed, for example, by Percy (1996). In his pioneering work, Zellner (1962, 1963) introduced a two-stage est”
Chain graph models of multivariate regression type for categorical data (2009, background): “Recently, [8] distinguished four types of chain graphs comprising the classical and the alternative [1] chain graph models, called type I and II, respectively.”