Xenbase Image ID: 130675

Figure 2. Inference error versus number of random valid edges provided. The proportional error (i.e., inference error) denotes the minimal cross-validation error divided by the minimal least-squares error of the linear regression without any regularization terms and averaged over five random networks and five random sets of valid edges. Zero to 25 (i.e., the number of all edges in each random network) prior edges are provided respectively. (A), (B) and (C) are the results of 4, 5 and 6 observations, respectively. The proportional errors decrease when more valid edges are provided. Prior connections appear to be more important when only a few observations are available. Image published in: Zheng Z et al. (2014) Copyright © 2014 Zheng et al. This image is reproduced with permission of the journal and the copyright holder. This is an open-access article distributed under the terms of the Creative Commons Attribution license Larger Image Printer Friendly View

Return to previous page