Bayesian inference for low-rank Ising networks

M. Marsman, Gunter Maris, Timo Bechger, Cornelis A.W. Glas

Research output: Contribution to journalArticleAcademicpeer-review

48 Citations (Scopus)
115 Downloads (Pure)


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
Original languageEnglish
Article number9050
JournalScientific reports
Issue number9050
Publication statusPublished - 2015


  • IR-99474
  • METIS-315933


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