Density estimation methods often involve kernels, but there are advantages to using splines. Especially if the shape of the density is known to be decreasing, or unimodal, or bimodal, or if the ...

Abstract: Probabilistic modeling is a core challenge in statistical machine learning. Tensor-based probabilistic graph methods address interpretability and stability concerns encountered in neural ...

Abstract: Traffic density estimation plays a crucial role in traffic management. This paper introduces a novel approach for accurately estimating traffic density using a graph-based density estimation ...

This is the official PyTorch implementation of the density-based anomaly detector "MULDE" which is trained via score matching. The anomaly detector is proposed in the paper MULDE: Multiscale ...