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Alexandra Federer: Estimating networks using mutual information
Adviser: Prof. Dr. Marloes Maathuis
Co-Adviser: Dr. Markus Kalisch
July 2011
Abstract:
Identifying the relations between variables of a dataset and visualize these relationships in an independence network is important in many applications. We use the concepts of entropy and mutual information to estimate the dependency between two random variables. An advantage of this method in comparison to a correlation test is that mutual information measures also non-linear dependency. To estimate the correlation graph of a dataset, we construct a statistical test for zero mutual information. We analyze the performance of this method compared with the well-known method of estimating the correlation graph by defining a threshold for the mutual information regarding to ROC-curves.
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