Bayesian Inference for low-rank Ising networks

12 maart 2015
Auteur: Marsman, M., Maris, G., Bechger, T. M., & Glas, C. A. W.

Estimating the structure of Ising networks is a notoriously difficult problem. In this article it is demonstrated that using a latent variable representation of the Ising network, a full-data-information approach to uncover the network structure can be employed. 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. The full-data-information approach is illustrated with the estimation of dense networks.

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