I just read this article about the motivation for KDE. From what I understand, you are using Gaussian probability density distributions for each datapoint and then, depending on the selected kernel width, you get a KDE curve. I had a few questions:
- What is the kernel width defined as, for these Gaussian distributions? Is it just the variance?
- From what I understand, you add the probability densities corresponding to each point to get the full KDE. What does the resulting value curve physically mean? Is it some sort of cumulative probability density?