Latest News and Events

The SAMSI-FODAVA Workshop on Interactive Visualization and Analysis of Massive Data will be held on December 10-12, 2012.
Posted: October 02, 2012
The FODAVA Annual Meeting will immediately follow (Dec 12-13) the SAMSI/FODAVA joint workshop at the same location.
Posted: September 05, 2012
Many of the modern data sets such as text and image data can be represented in high-dimensional vector spaces and have benefited from computational methods that utilize advanced techniques from num
Posted: June 30, 2012

Visualizing Conditional Dependencies in Hyper-Graph Models

Sayan Mukherjee

A parameterization of hypergraphs based on the geometry of points in $rr^m$ is developed. Given this parameterization, informative priors on hypergraphs are induced by priors on point configurations via spatial processes. This prior specification is used to infer conditional independence models or Markov structure of a multivariate distribution. Specifically, we can recover both the factorization as well as the hyper Markov law. The advantages of this approach are greater control on the distribution of graph features than Erd"{o}s-R'{e}nyi random graphs, inference of factorizations that cannot be retreived by a graph alone, and Markov chain Monte Carlo algorithms that allow for local and global moves in graph space. We illustrate the utility of this parameterization and prior specification using simulations.