Bayesian inference for low-rank Ising networks
| Authors |
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| Publication date | 2015 |
| Journal | Scientific Reports |
| Article number | 9050 |
| Volume | Issue number | 5 |
| Number of pages | 7 |
| Organisations |
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| 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. |
| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1038/srep09050 |
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