J
john.casey
Dear All,
I was wondering if any one might help explain what is the best way to
generate random variables that are distributed according to a zipf
distribution with a slope of -1 and an output range of 1 to 30. I have
tried using cern's cern.jet.random.Distributions nextZipfInt(... ) (see
http://hoschek.home.cern.ch/hoschek/colt/V1.0.3/doc/cern/jet/random/Distributions.html)
but the output of this generator is severely clipped if I attempt to
change the output range using a simple rejection loop for variates not
in the range 1 to 30. I have also tried generating a series of data
points that are derived using zipf's law and have been able to
uniformly at random sample these values. This method seems to work
quite well, and the distribution of sampled values better approximates
zipf's law than using the cern random generator. In terms of
methodology is this method acceptable?? it seems to be a bit of a
kludge to me. But I am having difficulty finding a better solution.
Any ideas ?? thanks.
I was wondering if any one might help explain what is the best way to
generate random variables that are distributed according to a zipf
distribution with a slope of -1 and an output range of 1 to 30. I have
tried using cern's cern.jet.random.Distributions nextZipfInt(... ) (see
http://hoschek.home.cern.ch/hoschek/colt/V1.0.3/doc/cern/jet/random/Distributions.html)
but the output of this generator is severely clipped if I attempt to
change the output range using a simple rejection loop for variates not
in the range 1 to 30. I have also tried generating a series of data
points that are derived using zipf's law and have been able to
uniformly at random sample these values. This method seems to work
quite well, and the distribution of sampled values better approximates
zipf's law than using the cern random generator. In terms of
methodology is this method acceptable?? it seems to be a bit of a
kludge to me. But I am having difficulty finding a better solution.
Any ideas ?? thanks.