Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...

We propose a method for reconstructing a probability density function (pdf) from a sample of an n-dimensional probability distribution. The method works by iteratively applying some simple ...

Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for 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 ...

This is a preview. Log in through your library . Abstract We examine large-sample properties of cross-validation for estimating cell probabilities, starting from a completely general measure of loss.

Abstract: Parameter estimation of probability density functions is one of the major steps in the area of statistical image and signal processing. In this paper we explore several properties and ...

Our eLibrary offers over 25,000 IMF publications in multiple formats. Building on the widely-used double-lognormal approach by Bahra (1997), this paper presents a multi-lognormal approach with ...