Results 11 to 20 of about 1,733,991 (287)
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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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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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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Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation
Mathematics, 2020 A soft-constrained neural network for density estimation (SC-NN-4pdf) has recently been introduced to tackle the issues arising from the application of neural networks to density estimation problems (in particular, the satisfaction of the second ...
Edmondo Trentin
doaj +1 more source
Remote Sensing, 2020 We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm ...
Claude Cariou +2 more
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Multiscale Feature Adaptive Integration for Crowd Counting in Highly Congested Scenes
IEEE Access, 2022 Due to extreme scale variations in highly congested scenes, the accuracy of CNN-based crowd counting approaches still has considerable room for further improvements.
Hui Gao +4 more
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Kernel density estimation via diffusion [PDF]
, 2010 We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate.
Botev, Z. I. +2 more
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Density estimates for Canada lynx vary among estimation methods
Ecosphere, 2021 Unbiased population density estimates are critical for ecological research and wildlife management but are often difficult to obtain. Researchers use a variety of sampling and statistical methods to generate estimates of density, but few studies have ...
D. Doran‐Myers +8 more
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Mathematics, 2022 A difficult and open problem in artificial intelligence is the development of agents that can operate in complex environments which change over time. The present communication introduces the formal notions, the architecture, and the training algorithm of
Edmondo Trentin
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Bayesian multivariate mixed-scale density estimation [PDF]
, 2014 Although continuous density estimation has received abundant attention in the Bayesian nonparametrics literature, there is limited theory on multivariate mixed scale density estimation.
Canale, Antonio, Dunson, David B.
core +3 more sources