In this paper, we propose a method for robust kernel density estimation. We interpret a KDE with Gaussian kernel as the inner product between a mapped test point and the centroid ...
Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches ...
Abstract. A variational problem characterizing the density estimator defined by the maximum a posteriori method with Gaussian process priors is derived. It is shown that this probl...
In this paper, we consider power spectral density estimation of bandlimited, wide-sense stationary signals from sub-Nyquist sampled data. This problem has recently received attent...
Michael A. Lexa, Mike E. Davies, John S. Thompson,...
Consider the problem of estimating the -level set G = {x : f(x) } of an unknown d-dimensional density function f based on n independent observations X1, . . . , Xn from the densi...