Let me try to explain what's going on here.
First look at a pair of discrete random variables, the scores on two dice.
X and Y each take values {1,2,3,4,5,6} with probability 1/6, and all
other values with probability 0.
Equivalently, the probability mass functions f_X and f_Y are each
David A. Heiser <[EMAIL PROTECTED]> wrote in message
001a01bfe48f$242194a0$[EMAIL PROTECTED]:">news:001a01bfe48f$242194a0$[EMAIL PROTECTED]:
> First Gautam Sethi used the term "convolution" for the
> product to two (uniform) densities. Aniko responded
> with a definition of convolution as the sum
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David A. Heiser wrote:
> First Gautam Sethi used the term "convolution" for the product to
> two (uniform) densities. Aniko responded with a definition of
> convolution as the s
If random variable x has density f and random variable y has density g, then
random variable t = x + y has density
h(t) = {the integral from zero to t of} f(t-lambda) times g(lambda) d(lambda).
(i.e. the density of the sum is the convolution of the densities)
At 18:35 -0700 07/02/2000, David A.
First Gautam Sethi used the term "convolution" for the product
to two (uniform) densities. Aniko responded with a definition of convolution as
the sum of two random variables. Then Jan de Leeuw stated that "convolution is
the distribution of the sum". Herman Rubin stated that "convolution is
