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Generating Markov Equivalent Maximal Ancestral Graphs by Single Edge Replacement
Markov Equivalent Maximal Ancestral Single Edge Replacement
2012/9/19
Maximal ancestral graphs(MAGs) are used to encode conditional independence relations in DAG models with hidden variables. Di erent MAGs may represent the same set of con-ditional independences and are...
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
DAG maximal ancestral graph Markov equivalence
2012/9/18
It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one repre...
Discriminating different classes of biological networks by analyzing the graphs spectra distribution
Discriminating different classes biological networks analyzing the graphs spectra distribution
2012/9/17
The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of com-plex networks, such as highly connected...
Let (V,A) be a weighted graph with a finite vertex set V,with a symmetric matrix of nonnegative weightsAand with Laplacian ∆. LetS∗: V ×V 7→ R be a symmetric kernel defined on the vertex s...
A note on global Markov properties for mixed graphs
Graphical models separation global Markov property
2011/7/19
Global Markov properties in mixed graphs are usually formulated in terms of the path-oriented m-separation or by use of augmented graphs (similar to moral graphs in the case of directed acyclic graphs...
Error Prediction and Model Selection via Unbalanced Expander Graphs
Error Prediction Model Selection Unbalanced Expander Graphs
2010/10/19
We investigate deterministic design matrices for the fundamental problems of error prediction and model selection. Our deterministic design matrices are constructed from unbalanced expander graphs, a...
Estimation of Gaussian graphs by model selection
Gaussian graphical model Random matrices Model selection Penalized empirical risk
2009/9/16
We investigate in this paper the estimation of Gaussian graphs by model selection from a non-asymptotic point of view. We start from a $n$-sample of a Gaussian law $mathbb{P}_C$ in $mathbb{R}^p$ and f...