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

The kernel density estimation function uses a smooth peak function to simulate the real probability distribution curve by fitting the observed data points.

Syntax

`select kernel_density_estimation(bigint stamp, double value, varchar kernelType)`

Parameters

**Parameter****Description**stamp

The UNIX timestamp. Unit: seconds.

value

The observed value.

kernelType

box: rectangle window

epanechniov: Epanechnikov curve

gausener: Gaussian curve

Result

**Display item****Description**unixtime

The timestamp of the source data.

real

The observed value.

pdf

The probability of each observed data point.

Example

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: