||Dependency estimation is a significant part of knowledge
discovery and allows strategic decisions based on this information.
Many dependency estimation algorithms require a large amount of data for a good
estimation. But data can be expensive, as an example experiments in material sciences,
consume material and take time and energy.
As we have the challenge of expensive data collection, algorithms need to be data
efficient. But there is a trade-off between the amount of data and the quality of the
estimation. With a lack of data comes an uncertainty of the estimation. However, the
algorithms do not always quantify this uncertainty. As a result, we do not know if we
can rely on the estimation or if we need more data for an accurate estimation.
In this bachelor’s thesis we compare different state-of-the-art dependency estimation
algorithms using a list of criteria addressing the above-mentioned challenges. We partly
developed the criteria our self as well as took them from relevant publications. Many
of the existing criteria where only formulated qualitative, part of this thesis is to make
these criteria measurable quantitative, where possible, and come up with a systematic
approach of comparison for the rest.
We also conduct a quantitative analysis of the dependency estimation algorithms by
experiment on well-established and representative data sets that performed well in the