| 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 allow 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. Many 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 for the experiment. 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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