| Kurzfassung
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In our data-driven world, where large amounts of data are collected in all kinds of environments, data analysis rises in importance. How different variables influence each other is a significant part of knowledge discovery and allows strategic decisions based on this knowledge. Therefore 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. In addition, most of these dependency estimation algorithms need large data sets to return a good estimation. In practice, gathering this amount of data may be costly, especially when collected in experiments with high costs for materials or infrastructure needed. In this study, different state-of-the-art dependency estimation algorithms are compared by a list of different criteria. This study focuses 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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