kernel density estimation is a non-parametric way to estimate the probability density function of a random variable.
I'm trying to create a simple densityplot in R in ggplot2. Here's my code which works great. d <- …
r ggplot2 kernel-densityI need to find as precisely as possible the peak of the kernel density estimation (modal value of the continuous …
r kernel-densityI'm trying to use the scipy.stats.gaussian_kde class to smooth out some discrete data collected with latitude and …
scipy multidimensional-array kernel-densityI'm looking to create multiple density graphs, to make an "animated heat map." Since each frame of the animation should …
r plot ggplot2 kernel-densityI have a x,y distribution of points for which I obtain the KDE through scipy.stats.gaussian_kde. This …
python integration kernel-density probability-densityUpdate: Weighted samples are now supported by scipy.stats.gaussian_kde. See here and here for details. It is currently …
python statistics scipy kernel-densityI have a density estimate (using density function) for my data learningTime (see figure below), and I need to find …
r probability kernel-density probability-density density-plotI have a collection of measured tree diameters and am trying to plot a histogram with a kernel density estimation …
python matplotlib kernel-density seaborn