Kernel Density Estimation

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Kernel density estimation is a useful statistical method to estimate the overall shape of a random variable distribution. In other words, kernel density estimation, also known as KDE, helps us to “smooth” and explore data that doesn’t follow any typical probability density distribution, such as normal distribution, binomial distribution, etc. In this tutorial, we will show you how to create an interactive kernel density estimation in Javascript and plot the result using the Highcharts library. Let’s first explore the KDE plot; then we will dive into the code. The demo below displays a Gaussian kernel density estimate of a random dataset:
See the Pen <a href="">Kernel Density Estimation (density plot with Gaussian kernel)</a> by mustapha mekhatria (<a href="">@mushigh</a>) on <a href="">CodePen</a>. This chart helps us to estimate the probability distribution of our random data set, and we can see that the data are concentrated mainly at the beginning and at the end of the chart. Basically, for each data points in red, we plot a Gaussian kernel function in orange, then we sum all the kernel functions together to create the density estimate in blue (see demo):

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