Bibliography | Schmidt, Jan-Oliver: Adaptive Kernel Density Estimation on Large Datasets. University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Bachelor Thesis (2017). 45 pages, english.
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CR-Schema | G.3 (Probability and Statistics)
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Abstract | In application fields such as data mining or uncertainty quantification very large, high dimensional datasets are rather common. While there are several different approaches to kernel density estimation, they all of them share the disadvantage of being very time consuming, when the sample set is really large. In this thesis, we want to overcome this issue by sub-sampling the dataset and choosing an adaptive kernel density estimator, which is as easy and time-saving, while still being as accurate as possible. Apart from being directly compared to another proved efficient density estimation technique, the Rosenblatt transformation is adducted to serve as an application example.
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Full text and other links | PDF (612286 Bytes) Access to students' publications restricted to the faculty due to current privacy regulations |
Department(s) | University of Stuttgart, Institute of Parallel and Distributed Systems, Simulation of Large Systems
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Superviser(s) | Pflüger, Jun.-Prof. Dirk; Franzelin, Fabian |
Entry date | September 28, 2018 |
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