The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For i.i.d. data several solutions hav...
In this paper, we suggest to model priors on human motion by means of nonparametric kernel densities. Kernel densities avoid assumptions on the shape of the underlying distribution...
Thomas Brox, Bodo Rosenhahn, Daniel Cremers, Hans-...
In this paper, a new segmentation approach for sets of 3D unorganized points is proposed. The method is based on a clustering procedure that separates the modes of a non-parametri...
Umberto Castellani, Marco Cristani, Vittorio Murin...
We present a nonparametric method for galaxy clustering in astronomical sky surveys. We show that the cosmological definition of clusters of galaxies is equivalent to density cont...
This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density ...