testing unfaithful gaussian graphical model
Testing Unfaithful Gaussian Graphical Models
De Wen Soh, Sekhar C. Tatikonda
Neural Information Processing Systems http://nips.cc/
Testing Unfaithful Gaussian Graphical Models
The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence. Specifically if a node set $S$ graph separates nodes $u$ and $v$ then $X_u$ is conditionally independent of $X_v$ given $X_S$. The opposite direction need not be true, that is, $X_u \perp X_v \mid X_S$ need not imply $S$ is a node separator of $u$ and $v$.
Testing Unfaithful Gaussian Graphical Models
The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence. Specifically if a node set S graph separates nodes u and v then X_u is conditionally independent of X_v given X_S . The opposite direction need not be true, that is, X_u \perp X_v \mid X_S need not imply S is a node separator of u and v . In this paper we provide a characterization of faithful relations and then provide an algorithm to test faithfulness based only on knowledge of other conditional relations of the form X_i \perp X_j \mid X_S .
Testing Unfaithful Gaussian Graphical Models
Soh, De Wen, Tatikonda, Sekhar C.
The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence. Specifically if a node set $S$ graph separates nodes $u$ and $v$ then $X_u$ is conditionally independent of $X_v$ given $X_S$. The opposite direction need not be true, that is, $X_u \perp X_v \mid X_S$ need not imply $S$ is a node separator of $u$ and $v$. In this paper we provide a characterization of faithful relations and then provide an algorithm to test faithfulness based only on knowledge of other conditional relations of the form $X_i \perp X_j \mid X_S$. Papers published at the Neural Information Processing Systems Conference.