The KDE procedure performs either univariate or bivariate kernel density estimation. Statistical density estimation involves approximating a hypothesized probability density function from observed ...
Abstract: In this work, a kernel-based Ensemble Gaussian Mixture Probability Hypothesis Density (EnGM-PHD) filter is presented for multi-target filtering applications. The EnGMPHD filter combines the ...
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 ...
This is a preview. Log in through your library . Abstract Sequential Monte Cario (SMC) methods, also known as particle filters, are simulation-based recursive algorithms for the approximation of the a ...
Gordon Lee et al introduce a data-driven and model-agnostic approach for computing conditional expectations. The new method combines classical techniques with machine learning methods, in particular ...
Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 45, No. 2 (1996), pp. 135-150 (16 pages) Motivated by the line transect aerial surveys of Southern Bluefin Tuna in the sea ...
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