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Bayesian inference for low-rank Ising networks
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 dis...
Tallennettuna:
| Julkaisussa: | Sci Rep |
|---|---|
| Päätekijät: | , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
Nature Publishing Group
2015
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4356966/ https://ncbi.nlm.nih.gov/pubmed/25761415 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/srep09050 |
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