Name
Abstract | ||
|---|---|---|
Markov Random Fields, or undirected graphical models are widely used to model high-dimensional multivariate data. Classical instances of these models, such as Gaussian Graphical and Ising Models, as well as recent extensions (Yang et al., 2012) to graphical models specified by univariate exponential families, assume all variables arise from the same distribution. Complex data from high-throughput genomics and social networking for example, often contain discrete, count, and continuous variables measured on the same set of samples. To model such heterogeneous data, we develop a novel class of mixed graphical models by specifying that each node-conditional distribution is a member of a possibly different univariate exponential family. We study several instances of our model, and propose scalable M-estimators for recovering the underlying network structure. Simulations as well as an application to learning mixed genomic networks from next generation sequencing and mutation data demonstrate the versatility of our methods. |
Year | Venue | Field |
|---|---|---|
2014 | JMLR Workshop and Conference Proceedings | Random field,Computer science,Exponential family,Markov chain,Complex data type,Theoretical computer science,Gaussian,Artificial intelligence,Graphical model,Univariate,Machine learning,Estimator |
DocType | Volume | ISSN |
Conference | 33 | 1938-7288 |
Citations | PageRank | References |
12 | 1.02 | 6 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eunho Yang | 1 | 132 | 27.43 |
| Yulia Baker | 2 | 14 | 1.41 |
| Pradeep D. Ravikumar | 3 | 2185 | 155.99 |
| Genevera I. Allen | 4 | 89 | 11.18 |
| Zhandong Liu | 5 | 50 | 7.60 |