Name
Abstract | ||
|---|---|---|
We define a class of graphs that summarize in a compact visual way the interaction structure between binary multivariate characteristics. This allows studying the conditional dependency structure between the underlying stochastic variables at a finer scale than with classical probabilistic Graphical Models. A model selection strategy is derived based on an iterative optimization procedure and the consistency problem is discussed. We include the analysis of two data-sets to illustrate the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-16952-6_44 | IBERAMIA |
Keywords | Field | DocType |
conditional dependency structure,multivariate binary data,consistency problem,finer scale,iterative optimization procedure,model selection strategy,classical probabilistic,graphical models,interaction structure,binary multivariate characteristic,interaction graph,data visualization,model selection | Graph,Data visualization,Multivariate statistics,Computer science,Model selection,Theoretical computer science,Dependency structure,Artificial intelligence,Binary data,Graphical model,Machine learning,Binary number | Conference |
Volume | ISSN | ISBN |
6433 | 0302-9743 | 3-642-16951-1 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Johan Van Horebeek | 1 | 20 | 3.38 |
| Jesús Emeterio Navarro-Barrientos | 2 | 0 | 1.69 |