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When learning a directed acyclic graph (DAG) model via observational data, one gener-ally cannot identify the underlying DAG, but can potentially obtain a Markov equivalence class. The size (the numbe...
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Mar...
This paper introduces a novel framework, HodgeR-ank on Random Graphs (HRRG), based on paired comparison,for subjective video quality assessment. Two types of random graph models are studied, i.e., Erd...
When learning a directed acyclic graph (DAG) model via observational data, one gener-ally cannot identify the underlying DAG, but can potentially obtain a Markov equivalence class. The size (the numbe...
This paper introduces a novel framework, HodgeR-ank on Random Graphs (HRRG), based on paired comparison,for subjective video quality assessment. Two types of random graph models are studied, i.e., Erd...
We prove a central limit theorem for the components of the largest eigenvector of the adjacency matrix of a one-dimensional random dot product graph whose true latent positions are unknown. In particu...
We consider the problem of vertex classification for graphs constructed from the latent position model. It was shown previously that the approach of embedding the graphs into some Euclidean space foll...
Marginal AMP Chain Graphs     Marginal  AMPChain Graphs       2013/6/13
We present a new family of graphical models that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because each of them can be seen as the r...
In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modeling of such different data-types, based on global parame...
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability ...
This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversible MCMC on Markov eq...
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...
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...
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...

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