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Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Directed acyclic graphs Markov equivalence class Size distribution Causal- ity
2016/1/26
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...
Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs
Sparse graphical model Reversible Markov chain Markov equivalence class
2016/1/25
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...
HodgeRank on Random Graphs for Subjective Video Quality Assessment
Video Quality Assessment Paired Comparison HodgeRank Random Graphs Persistence Homology
2016/1/25
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...
Counting and Exploring Sizes of Markov Equivalence Classes of Directed Acyclic Graphs
Directed acyclic graphs Markov equivalence class Size distribution Causal- ity
2016/1/20
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...
HodgeRank on Random Graphs for Subjective Video Quality Assessment
Video Quality Assessment Paired Comparison HodgeRank Random Graphs Persistence Homology
2016/1/20
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...
Mixing Times for Random Walks on Geometric Random Graphs
Mixing Times Random Walks Geometric Random Graphs
2015/7/10
A geometric random graph, G^d(n,r), is formed as follows: place n nodes uniformly at random onto the surface of the d-dimensional unit torus and connect nodes which are within a distance r of each oth...
Fastest Mixing Markov Chain on Graphs with Symmetries
Markov chains fast mixing eigenvalue optimization semidefi nite programming
2015/7/9
We show how to exploit symmetries of a graph to efficiently compute the fastest mixing Markov chain on the graph (i.e., find the transition probabilities on the edges to minimize the second-largest ei...
Network Lasso: Clustering and Optimization in Large Graphs
Convex Optimization ADMM Network Lasso
2015/7/8
Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimiza...
A limit theorem for scaled eigenvectors of random dot product graphs
limit theorem scaled eigenvectors random dot product graphs
2013/6/14
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...
Out-of-sample Extension for Latent Position Graphs
out-of-sample extension inhomogeneous random graphs latent position model convergence of eigenvectors
2013/6/14
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...
Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs
Causal inference Interventions BIC Graphical model Maximum likelihood estimation Greedy equivalence search
2013/4/27
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...
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness
Learning AMP Chain Graphs some Marginal Models Thereof under Faithfulness
2013/4/27
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 ...
Supplement to "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs"
Sparse graphical model Reversible Markov chain Markov equivalence class
2013/4/27
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...
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...