A Graphical Method for Model Selection

A Graphical Method for Model Selection

In this paper, we present a graphical method for selection of a good model among the several competitive models for the same data set. The proposed method not only selects the model but also tests the equal prediction accuracy of the models. The results of the proposed method are compared with that of the model selection using Friedman test.

Strategies for Graphical Model Selection

Efficient Latent Variable Graphical Model Selection via Split Bregman Method

We consider the problem of covariance matrix estimation in the presence of latent variables. Under suitable conditions, it is possible to learn the marginal covariance matrix of the observed variables via a tractable convex program, where the concentration matrix of the observed variables is decomposed into a sparse matrix (representing the graphical structure of the observed variables) and a l...

Active Learning for Undirected Graphical Model Selection

This paper studies graphical model selection, i.e., the problem of estimating a graph of statistical relationships among a collection of random variables. Conventional graphical model selection algorithms are passive, i.e., they require all the measurements to have been collected before processing begins. We propose an active learning algorithm that uses junction tree representations to adapt f...

Active Learning Algorithms for Graphical Model Selection

The problem of learning the structure of a high dimensional graphical model from data has received considerableattention in recent years. In many applications such as sensor networks and proteomics it is often expensive toobtain samples from all the variables involved simultaneously. For instance, this might involve the synchronization of alarge number of sensors or the tagging of a...