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- 1Graphical model
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Fall 2015
Graphical models are frequently used to explore networks among a set of variables. Several methods for estimating sparse graphs have been proposed and their theoretical properties have been explored. There are also several selection criteria to select the optimal estimated models. However, their...
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Fall 2018
Gaussian processes are flexible probabilistic models for regression and classification. However, their success hinges on a well-specified kernel that can capture the structure of data. For complex data, the task of hand crafting a kernel becomes daunting. In this thesis, we propose new methods...