In the frame of this bachelor thesis, density estimation with the sparse grid combination technique was implemented and integrated into the sparseSpACE framework. The sparse grid combination technique is used to compute a sparse grid, whereby a specific sequence of small anisotropic full grids is combined linearly, to tackle the curse of dimensionality. The usage of mass lumping in the density estimation process is also explored, which still achieves relatively good results compared to the standard combination method. The density function is estimated for different data sets using the combination technique and compared with the full grid solution in regards to different error norms. The test results show that we achieve a good estimate of the density function while simultaneously reducing the number of grid points used.     «

In the frame of this bachelor thesis, density estimation with the sparse grid combination technique was implemented and integrated into the sparseSpACE framework. The sparse grid combination technique is used to compute a sparse grid, whereby a specific sequence of small anisotropic full grids is combined linearly, to tackle the curse of dimensionality. The usage of mass lumping in the density estimation process is also explored, which still achieves relatively good results compared to the...     »