Bayesian predictive density estimation represents a cornerstone of modern statistical inference by integrating prior knowledge with observed data to produce a predictive distribution for future ...

Abstract: Particle filters (PFs) are widely used for state estimation in signal processing. However, the standard PFs suffer from weight degeneracy and sample impoverishment. To overcome these, we ...

Abstract: Aiming at the problem that the traditional photovoltaic output parametric model presets the distribution and is difficult to describe the meteorological randomness, this paper proposes a ...

This repository contains code and data for crowd density estimation using guided diffusion and super-resolution approaches. The project provides data preprocessing utilities, training/test scripts, a ...

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