Kernel density estimation is a non-parametric test method. It is used to estimate unkonwn density functions in probability theory.

Kernel density estimation functions use a smooth peak function to simulate the real
probability distribution curve by fitting the observed data points.

- Function format:
`select kernel_density_estimation(bigint stamp, double value, varchar kernelType)`

- Parameters
Parameter Description stamp Unix timestamp. Unit: second. value Observed value. kernelType - box: rectangle.
- epanechnikov: Epanechnikov curve.
- gaussian: Gaussian curve.

- Output result
Display item Description unixtime The time of the source data. real Observed value. pdf The probability of each point. - Examples
- Sample code:
`* | select date_trunc('second', cast(t1[1] as bigint)) as time, t1[2] as real, t1[3] as pdf from ( select kernel_density_estimation(time, num, 'gaussian') as res from ( select __time__ - __time__ % 10 as time, COUNT(*) * 1.0 as num from log group by time order by time) ), unnest(res) as t(t1) limit 1000`

- Sample result:

- Sample code: