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  • Difference between KDE, MLE and EM for density estimation
    5 I'm reviewing kernel density estimation (KDE), maximum likelihood estimation (MLE) and expectation maximization (EM) algorithm for density estimation and struggling to differentiate what each algorithm's merits are compared to it's predecessor
  • Kernel density estimation incorporating uncertainties
    When visualising one-dimensional data it's common to use the Kernel Density Estimation technique to account for improperly chosen bin widths When my one-dimensional dataset has measurement
  • Quantile of kernel density estimator - Cross Validated
    Quantile estimation is in my view best approached directly With kernel density estimation you can't escape the need for choice of kernel shape and width -- even if the choice is made by program defaults Such variations are especially important near P = 0 P or P = 1 P The method of Harrell and Davis is especially worth mentioning (reference at )
  • Finding the Peak of a Kernel Density Estimator - Cross Validated
    Less obvious than looking for a peak in density, but still worth a try, is to look for a shoulder on a quantile plot Kernel estimation is an excellent method, especially when bimodality or multimodality is a possibility The suggestion, however, is that it may be regarded as an independent method of assessing modality
  • r - Peak of the kernel density estimation - Stack Overflow
    I need to find as precisely as possible the peak of the kernel density estimation (modal value of the continuous random variable) I can find the approximate value:
  • Adaptive Bandwidth Kernel Density Estimation - Stack Overflow
    This package implements adaptive kernel density estimation algorithms for 1-dimensional signals developed by Hideaki Shimazaki This enables the generation of smoothed histograms that preserve important density features at multiple scales, as opposed to naive single-bandwidth kernel density methods that can either over or under smooth density
  • python - Kernel Density Estimation (KDE) implementation in pytorch or . . .
    I created a differentiable PyTorch implementation of Kernel Density Estimation (KDE) called torch-kde One can create a KernelDensity object, similarly to how one would do it in scikit-learn





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