Bayesian inference for low-rank Ising networks.

ABSTRACT: Estimating the structure of Ising networks is a notoriously difficult problem. We demonstrate that using a latent variable representation of the Ising network, we can employ a full-data-information approach to uncover the network structure. Thereby, only ignoring information encoded in the prior distribution (of the latent variables). The full-data-information approach avoids having to compute the partition function and is thus computationally feasible, even for networks with many nodes. We illustrate the full-data-information approach with the estimation of dense networks.

SUBMITTER: Marsman M 

PROVIDER: S-EPMC4356966 | biostudies-other | 2015

REPOSITORIES: biostudies-other