Using sparse grids for density estimation can reduce computational expenses in comparison to more popular kernel density methods by reducing the amount of examined points, especially for datasets in higher dimensions. The goal of this thesis is to study two recently added algorithms for density difference and density ratio estimation in the SG++ code library for sparse grids. Using a custom pipeline, experiments studying the behavior and accuracy of the algorithms compare the sparse grid results to the analytical solution and to additional kernel based density estimation methods. We aim to visualize and quantify the differences between sparse grid based solutions and other solutions to demonstrate their accuracy and usability for future use in high-dimensional applications and time-series segmentation.     «

Using sparse grids for density estimation can reduce computational expenses in comparison to more popular kernel density methods by reducing the amount of examined points, especially for datasets in higher dimensions. The goal of this thesis is to study two recently added algorithms for density difference and density ratio estimation in the SG++ code library for sparse grids. Using a custom pipeline, experiments studying the behavior and accuracy of the algorithms compare the sparse grid results...     »