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JMLR
2008
141views more  JMLR 2008»
13 years 7 months ago
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
In graphical modelling, a bi-directed graph encodes marginal independences among random variables that are identified with the vertices of the graph. We show how to transform a bi...
Mathias Drton, Thomas S. Richardson
CORR
2011
Springer
152views Education» more  CORR 2011»
13 years 2 months ago
Topology Discovery of Sparse Random Graphs With Few Participants
We consider the task of topology discovery of sparse random graphs using end-to-end random measurements (e.g., delay) between a subset of nodes, referred to as the participants. T...
Animashree Anandkumar, Avinatan Hassidim, Jonathan...
CAAN
2007
Springer
14 years 2 months ago
Cleaning Random d-Regular Graphs with Brushes Using a Degree-Greedy Algorithm
In the recently introduced model for cleaning a graph with brushes, we use a degree-greedy algorithm to clean a random d-regular graph on n vertices (with dn even). We then use a d...
Margaret-Ellen Messinger, Pawel Pralat, Richard J....
GD
2000
Springer
13 years 11 months ago
User Preference of Graph Layout Aesthetics: A UML Study
The merit of automatic graph layout algorithms is typically judged on their computational efficiency and the extent to which they conform to aesthetic criteria (for example, minimi...
Helen C. Purchase, Jo-Anne Allder, David A. Carrin...
ICCV
2007
IEEE
14 years 10 months ago
LogCut - Efficient Graph Cut Optimization for Markov Random Fields
Markov Random Fields (MRFs) are ubiquitous in lowlevel computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to -expansion...
Victor S. Lempitsky, Carsten Rother, Andrew Blake