Eugen PircalabeluElvira, LucasLucasElvira2025-05-142025-05-142025-05-142024https://hdl.handle.net/2078.2/39656Penalized Gaussian Graphical Models aim to describe the graphical structure underlying the variables. Transfer Learning allows to add one or more auxiliary studies as additional information for the model estimation, which could then lead to enhanced performance of the estimator. In this thesis, we aim to explain the theory behind Transfer Learning applied to Gaussian Graphical Models and compare the performance of the corresponding estimator, Trans-CLIME, against two well-known competitors, the CLIME and Glasso estimators.Gaussian Graphical ModelsTransfer LearningGlassotext::thesis::master thesisthesis:48152