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2011

2011

Proof. As the edges in GC represent causal relations, a path of length 0 (no edge) is not considered a causal relation, and… Expand

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2010

2010

We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets… Expand

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2009

2009

Marginal independence constraints play an important role in learning with graphical models. One way of parameterizing a model of… Expand

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Highly Cited

2008

Highly Cited

2008

Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are… Expand

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2008

2008

‘Iterative conditional fitting’ is a recently proposed algorithm that can be used for maximization of the likelihood function in… Expand

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2004

2004

Ancestral graph models, introduced by Richardson and Spirtes (2002), generalize both Markov random fields and Bayesian networks… Expand

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2004

2004

For an ancestral graph with four vertices, we show that the associated binary model exhibits different properties than the… Expand

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Highly Cited

2002

Highly Cited

2002

Abstract : This paper introduces a class of graphical independence models that is closed under marginalization and conditioning… Expand

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2002

2002

Ancestral graphs are a class of graphs that encode conditional independence relations arising in DAG models with latent and… Expand

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Highly Cited

1996

Highly Cited

1996

Directed acyclic graphs have been used fruitfully to represent causal structures (Pearl 1988). However, in the social sciences… Expand

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