Before coding this in C, I wanted to test the idea out in R. But I'm unsure if the theory is well-founded.
I have a (user-supplied) black-box function which takes R^n -> R^3 and a defined domain for each of the input reals. I want to send some samples through the box to determine an approximation of the convex hull of the function's range. (I'll use the library from http://www.qhull.org to compute the convex hull from the function's outputs.) My plan is to use the permutation of the min and max values for the n inputs along with k-1 samples w/in [min,max], but I want the adjust the k samples a bit to avoid sampling bias. To make it simpler, let's set the domain to [0,1]. Then, K = { 1/k, 2/k, ... (k-1)/k } One reasonably easy possibility is to add to each Kn a linear RV in, say, [-1/k²,1/k²]. Would a normal RV be better? Some other bell-shaped RV? Does adding a bit (but not too much) of randomness to the input values have reason at all? -JimC -- James Cloos <cl...@jhcloos.com> OpenPGP: 1024D/ED7DAEA6 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
