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...

书目详细资料
Main Authors:	Frot, B, Jostins, L, McVean, G
格式:	Journal article
出版:	Taylor and Francis 2018

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