Good Evening,
I'm trying to follow a paper from 1950 on the convolution of uniform
distributions at:
http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&id=pdf_1&handle=euclid.aoms/1177729446
Under section 2, they define the probability density of a uniform
distribution as:
f_i(x_i)=[epsilon(x_i) - epsilon(x_i - a_i)] / a_i
(a_i > 0 ; i = 1,2,...,n)
where epsilon(x - c) is unity for x >= c and zero elsewhere.
I can't for the life of me see how this defines a uniform distribution??
Any help in explaining this would be much appreciated.
Or, if anyone has a form for the convolution of n general uniform
distributions that may be of a simpler form....
I'm trying to compare the addition of a number of independent variables
that follow a gaussian distribution with the same variables that follow
a uniform distribution for an investigation into different error
propagation techniques.
Thank you all in advance,
Christopher Battles
