Graphical lasso for covariance structure learning in the high dimensional setting
This thesis considers the estimation of undirected Gaussian graphical models especially in the high dimensional setting where the true observations are assumed to be non-Gaussian distributed. The first aim is to present and compare the performances of existing Gaussian graphical model estimation meth...
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| Format: | Others |
| Language: | English |
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KTH, Matematisk statistik
2015
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176485 |