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Kernel Density Estimation: a novel tool for visualising training intensity distribution in biathlon [PDF]
Frontiers in Sports and Active LivingPurposeThis study introduces two-dimensional (2D) Kernel Density Estimation (KDE) plots as a novel tool for visualising Training Intensity Distribution (TID) in biathlon. The goal was to assess how KDE plots, alongside traditional training metrics, might
Craig A. Staunton +7 more
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DEMANDE: Density Matrix Neural Density Estimation
IEEE Access, 2023 Density estimation is a fundamental task in statistics and machine learning that aims to estimate, from a set of samples, the probability density function of the distribution that generated them.
Joseph A. Gallego-Mejia +1 more
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Scale and Background Aware Asymmetric Bilateral Network for Unconstrained Image Crowd Counting
Mathematics, 2022 This paper attacks the two challenging problems of image-based crowd counting, that is, scale variation and complex background. To that end, we present a novel crowd counting method, called the Scale and Background aware Asymmetric Bilateral Network ...
Gang Lv +4 more
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IEEE Transactions on Information Theory, 2023 We study the problem of estimating the density $f(\boldsymbol x)$ of a random vector ${\boldsymbol X}$ in $\mathbb R^d$. For a spanning tree $T$ defined on the vertex set $\{1,\dots ,d\}$, the tree density $f_{T}$ is a product of bivariate conditional densities. An optimal spanning tree minimizes the Kullback-Leibler divergence between $f$ and $f_{T}$.
Laszlo Gyorfi +2 more
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Remote Sensing in Ecology and Conservation, 2022 Accurate and precise density estimates are crucial for effective species management and conservation. However, efficient monitoring of mammal densities over large spatial and temporal scales is challenging.
Samantha S. Mason +5 more
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Density-Difference Estimation [PDF]
Neural Computation, 2013 We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small ...
Sugiyama, Masashi +5 more
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BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures
Journal of Statistical Software, 2021 BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models.
Riccardo Corradin +2 more
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Applied Sciences, 2022 We consider the use of remote sensing for large-scale monitoring of agricultural land use, focusing on classification of tillage and vegetation cover for individual field parcels across large spatial areas.
Markku Luotamo +2 more
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On Conditional Density Estimation [PDF]
Statistica Neerlandica, 2002 With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so‐called re‐weighted Nadaraya‐Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well‐known Nadaraya‐Watson smoother. With a detailed asymptotic analysis, we demonstrate that
de Gooijer, J.G., Zerom Godefay, D.
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Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation
Mathematics, 2020 A commonly used tool for estimating the parameters of a mixture model is the Expectation−Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator.
Branislav Panić +2 more
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