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Graphical Model Selection for Gaussian Conditional Random Fields in the Presence of Latent Variables
We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive...
Enregistré dans:
| Publié dans: | J Am Stat Assoc |
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| Auteurs principaux: | , , |
| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
Taylor & Francis
2018
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6636895/ https://ncbi.nlm.nih.gov/pubmed/31391793 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1080/01621459.2018.1434531 |
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