Function - Kernel Density Estimation (KDE)
"In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable."
from wikipedia.com
KDE function with optional kernel:
Republishing due to change of function.
deprecated script:
from wikipedia.com
KDE function with optional kernel:
- Uniform
- Triangle
- Epanechnikov
- Quartic
- Triweight
- Gaussian
- Cosinus
Republishing due to change of function.
deprecated script:
Versionsinformation:
added quartic and triweight kernels.
Versionsinformation:
- added placeholder for kernels(logistic, sigmoid, silverman)
- added kernel calculations for kernel(uniform, triangular, cosine)
Versionsinformation:
added calculations for kernels(logistic, sigmoid and silverman(Not working atm)
Versionsinformation:
removed silverman kernel, added highest value index line/label, nearest to 0 index as a dotted gray line.
Versionsinformation:
added extra stats/visuals to drawing function.