Sampling Networks from Their Posterior Predictive Distribution.

ABSTRACT: Recent research indicates that knowledge about social networks can be leveraged to increase efficiency of interventions (Valente, 2012). However, in many settings, there exists considerable uncertainty regarding the structure of the network. This can render the estimation of potential effects of network-based interventions difficult, as providing appropriate guidance to select interventions often requires a representation of the whole network. In order to make use of the network property estimates to simulate the effect of interventions, it may be beneficial to sample networks from an estimated posterior predictive distribution, which can be specified using a wide range of models. Sampling networks from a posterior predictive distribution of network properties ensures that the uncertainty

SUBMITTER: Goyal R 

PROVIDER: S-EPMC4203686 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature