Nowadays, analysing data from different classes or over a temporal grid has attracted great deal of interest. As result, various multiple graphical models for learning collection simultaneously have been derived by introducing sparsity in graphs and similarity across graphs. This paper focuses on the fused Lasso model which encourages not only shared pattern sparsity, but also values edges For ...

Infections Alice Barsoumian, MD; Katrin Mende, PhD; Carlos J. Sanchez Jr., PhD; Miriam L. Beckius, MPH; Joseph Wenke, PhD; Clinton K. Murray, MD; Kevin S. Akers, MD; San Antonio Military Medical Center, JBSA Fort Sam Houston, TX; Infectious Disease Clinical Research Program, Uniformed Services University, Bethesda, MD; Department of Extremity Trauma, United States Army Institute of Surgical Res...

This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model perspective. Well-known examples of graphical models include Bayesian networks , directed graphs representing a Markov chain, and undirected networks representing a Markov eld. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphica...

Graphical models provide a rich framework for summarizing the dependencies among variables. The graphical lasso approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an l1 penalty on the elements of the inverse co-variance matrix. Most algorithms for solving the graphical lasso problem do not scale to a very large n...

This paper is a multidisciplinary review of empirical, statistical learning from a graph-ical model perspective. Well-known examples of graphical models include Bayesian networks , directed graphs representing a Markov chain, and undirected networks representing a Markov eld. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphica...

In this paper, we present efficient layouts for complete graphs and star graphs. We show that an N-node complete graph can be optimally laid out using LN2/4] tracks for a colinear layout, and can be laid out in N4/16 + o(N4) area for a 2D layout. We also show that an N-node star graph can be laid out in N2/16 + o(N2) area, which is smaller than any possible layout of a similar-size hypercube. T...

A maximal prime subgraph decomposition junction tree (MPD-JT) is a useful computational structure that facilitates lazy propagation in Bayesian networks (BNs). A graphical method was proposed to construct an MPD-JT from a BN. In this paper, we present a new method from a relational database (RDB) perspective which sheds light on the semantic meaning of the previously proposed graphical algorithm.

We have developed a novel algorithm for integrating source localization and noise suppression based on a probabilistic graphical model of stimulus-evoked MEG/EEG data. Our algorithm localizes multiple dipoles while suppressing noise sources with the computational complexity equivalent to a single dipole scan, and is therefore more efficient than traditional multidipole fitting procedures. In si...