Graphical model selection for Gaussian conditional random fields in the presence of latent variables

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...

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Dades bibliogràfiques
Autors principals:	Frot, B, Jostins, L, McVean, G
Format:	Journal article
Publicat:	Taylor and Francis 2018

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