# simple sampling method for a Kernel Density Estimator

I have developed a simple Kernel Density Estimator in Java, based on a few dozen points (maybe up to one hundred or so) and a Gaussian kernel function. The implementation gives me the PDF and CDF of my probability distribution at any point.

I would now like to implement a simple sampling method for this KDE. An obvious choice would of course be to draw from the very set of points making up the KDE, but I would like to be able to retrieve points that are slightly different from the ones in the KDE.

I haven't found so far a sampling technique that I could easily implement to solve this problem (without depending on external libraries for numerical integration or complex computations). Any advices? I don't have specially strong requirements when it comes to precision or efficiency, my main concern is to have a sampling function that works and can be easily implemented. Thanks!

• This is detailed in page 5 of this document.
– user10525
Nov 15, 2012 at 18:21
• thanks, that was useful! And simpler than I thought ;-) Nov 15, 2012 at 19:49
• @user10525 the code provided is incorrect, it should be: rnorm(n, sample(dx$x, n, prob = dx$y, replace = TRUE), dx$bw) where dx is output from density function. Argument prob has to be provided because otherwise you sample uniformly. – Tim Dec 22, 2015 at 20:29 ## 1 Answer As mentioned by Procrastinator, there's a simple way to sample from a Kernel density estimator: 1. Draw one point$x_i$from the set of points$x_1$,...$x_n$included in the KDE 2. Once you have the point$x_i$, draw a value from the kernel associated with the point. In this case, draw from the Gaussian$\mathcal{N}(x_i,h)$centered at$x_i$and of variance$h$(the bandwidth) • (+1) For sharing your solution. – user10525 Nov 19, 2012 at 10:15 • Is$x_i$one of the original points? If so, looks like we don't really need to construct the actual KDE at all. Just sampling from one of the original points, and$N (x_i,h)\$ should suffice?
– Ram
Apr 8, 2013 at 23:19
• Yes indeed, if you are only using the KDE distribution for sampling, you do not need to explicitly construct the PDF: the only information necessary for the sampling operation is the set of points and the bandwidth. Apr 9, 2013 at 6:28
• just to add to Pierre Lison: In step 2.: For sampling from a Gaussian kernel, the bandwidth h should be taken as the standard deviation of the Gaussian distribution around the point x_i, not the variance.
– user98904
Dec 22, 2015 at 18:52
• Wouldn't you want to sample using standard deviation 1/h or something? As written, the less likely x_i is, the more likely you are to sample another unlikely point nearby because the standard deviation of N is low. Jul 3, 2019 at 21:24