Joint response graphs and separation induced by triangular systems

By Nanny Wermuth, D. R. Cox. 2004

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4dba13f60697e8fcae485e31e348b90f360ea81f

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

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, 15, 16, 23, 34, 42] and ancestral graph models [24].”
PROBABILITY DISTRIBUTIONS WITH SUMMARY GRAPH STRUCTURE (2010, methodology): “A summary graph provides the tool for detecting whether and for which of the generating dependences i≺ k, such distortions can occur due to direct or indirect confounding, see Wermuth and Cox (2008). We illustrate this here with two small examples where marginalising is represented by a crossed out ”
Causality: a Statistical View (2004, background): “This raises the interesting issue of the implications of the independencies implied by one ordering of the variables were the blocking of the variables to be rearranged (Wermuth & Cox, 2004).”
Chain graph models of multivariate regression type for categorical data (2009, background): “This factorization property is shared by all types of chain graphs; see [24] and [8].”
Estimating brain functional connectivity with sparse multivariate autoregression (2005, methodology): “Graphs, with only undirected edges, have been extensively used in the analysis of covariance relations (Wermuth & Lauritzen 1990; Wermuth & Cox 1998, 2004), but do not attempt causal interpretations.”
Discrete chain graph models (2009, background): “Four classes of Markov properties for chain graphs have been discussed in the literature, and we classify them as: Type I: the LWF or block concentration Markov property [12, 22]; Type II: the AMP or concentration regression Markov property [1]; Type III: a Markov property that is dual to the type I”