First, is this the correct C++ representation of the pdf gaussian function ?
float pdf_gaussian = ( 1 / ( s * sqrt(2*M_PI) ) ) * exp( -0.5 * pow( (x-m)/s, 2.0 ) );
Second, does it make sense of we do something like this ?
if(pdf_gaussian < uniform_random())
do something
else
do other thing
EDIT: An example of what exactly are you trying to achieve:
Say I have a data called Y1. Then a new data called Xi arrive. I want to see if I should associated Xi to Y1 or if I should keep Xi as a new data data that will be called Y2. This is based on the distance between the new data Xi and the existing data Y1. If Xi is "far" from Y1 then Xi will not be associated to Y1, otherwise if it is "not far", it will be associated to Y1. Now I want to model this "far" or "not far" using a gaussian probability based on the mean and stdeviation of distances between Y and the data that where already associated to Y in the past.