Today I needed a PRNG with a bised distribution, specifically following the Power Law distribution in order to simulate the "long tail" often seen in things like social networks data, search, and so forth.

I was not able to find ready to use code, but just a few formulas, so I wrote one in Ruby (I'm going to port this to C for Redis).

Maybe this will be useful to somebody else:

# Power law (log tail) distribution
# Copyright(C) 2010 Salvatore Sanfilippo
# this code is under the public domain

# min and max are both inclusive
# n is the distribution power: the higher, the more biased
def powerlaw(min,max,n)
    max += 1
    pl = ((max**(n+1) - min**(n+1))*rand() + min**(n+1))**(1.0/(n+1))
    (max-1-pl.to_i)+min
end

freq = {}
100000.times {
    n = powerlaw(0,100,2).to_i
    freq[n] = 0 if !freq[n]
    freq[n] += 1
}
(0..100).each{|x|
    puts "#{x} => #{freq[x]}"
}