title: 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.
authors.affiliation: Psychometric Research Center, Cito, the Netherlands.
authors.forename: Maarten
authors.initials: M
authors.lastname: Marsman
authors.affiliation: 1] Psychometric Research Center, Cito, the Netherlands [2] Department of Psychology, University of Amsterdam, the Netherlands.
authors.forename: Gunter
authors.initials: G
authors.lastname: Maris
authors.affiliation: Psychometric Research Center, Cito, the Netherlands.
authors.forename: Timo
authors.initials: T
authors.lastname: Bechger
authors.affiliation: Department of Research Methodology, Measurement and Data Analysis, University of Twente, the Netherlands.
authors.forename: Cees
authors.initials: C
authors.lastname: Glas
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical