| Kurzfassung
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In our data driven world, where large amounts of data are collected in all kind of environments, data analysis rises in importance. How different variables influence each other is an major part of knowledge discovery and allow strategic decisions based on this knowledge. Therefor high quality dependency estimation should be accessible to a variety of people. Many dependency estimation algorithms are difficult to use in a real-world setting. Many dependency estimation algorithms need very large data sets to return a good estimation. In practice gathering this amount of data may be costly. Especially when the data is collected in experiments with high cost for materials or infrastructure needed for the experiment. In this study different state-of-the-art dependency estimation algorithms are compared by a list of different criteria. The focus of this study is on data efficiency and uncertainty of the dependency estimation algorithms. The comparison includes a theoretical analysis and different experiments with dependency estimation algorithms that performed well in the theoretical analysis.
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