Results 11 to 20 of about 4,333,581 (301)
Statistical Science, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Simon Sheather
exaly +4 more sources
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
doaj +2 more sources
Density estimation on a network [PDF]
Computational Statistics & Data Analysis, 2021 38 pages, 13 ...
Yang Liu, David Ruppert
openaire +4 more sources
Probability hypothesis density filtering for real-time traffic state estimation and prediction [PDF]
, 2013 The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an
Mihaylova, Lyudmila +9 more
core +5 more sources
A probabilistic hypothesis density filter for traffic flow estimation in the presence of clutter [PDF]
, 2012 Prediction of traffic flow variables such as traffic volume, travel speed or travel time for a short time horizon is of paramount importance in traffic control.
Romain Billot +9 more
core +5 more sources
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
doaj +1 more source
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
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source