This Repository contains the Python Code for the Distribution Preserving Multiple Hypotheses Prediction (DPMHP) approach described in the Paper "Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling" by Tobias Leemann, Moritz Sackmann, Jörn Thielecke and Ulrich Hofmann, published at the European Symposium on Artificial Neural Networks (ESANN) 2021.
The code is a standalone Jupyter Notebook (DistributionPreservingMHP.ipynb), with precomputed models in the /data folder.