文章基本信息

标题：Bayesian inference for low-rank Ising networks
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作者：Maarten Marsman ; Gunter Maris ; Timo Bechger 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2015
卷号：5
DOI：10.1038/srep09050
出版社：Springer Nature
摘要：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.