Mean of Sampleset and powered Sampleset
- by Milla Well
I am working on an ICA implementation wich is based on the assumption, that all source signals are independent. So I checked on the basic concepts of Dependence vs. Correlation and tried to show this example on sample data
from numpy import *
from numpy.random import *
k = 1000
s = 10000
mn = 0
mnPow = 0
for i in arange(1,k):
a = randn(s)
a = a-mean(a)
mn = mn + mean(a)
mnPow = mnPow + mean(a**3)
print "Mean X: ", mn/k
print "Mean X^3: ", mnPow/k
But I couldn't produce the last step of this example E(X^3) = 0:
>> Mean X: -1.11174580826e-18
>> Mean X^3: -0.00125229267144
First value I would consider to be zero, but second value is too large, isn't it? Since I subtract the mean of a, I expected the mean of a^3 to be zero as well. Does the problem lie in
the random number generator,
the precision of the numerical values
in my misunderstanding of the concepts of mean and expected value?
