Tue Mar 30 16:36:33 CDT 2010
On Tue, Mar 30, 2010 at 16:32, nicky van foreest <email@example.com> wrote:
>> There are an infinite number of distributions that will have the same
>> skewness and kurtosis. However, it is reasonable to search for the
>> maximum entropy distribution satisfying those constraints. The normal
>> distribution is the maximum entropy distribution for a fixed mean and
>> The PDF will have the form:
>> pdf(x) = c * exp(- lagrange * (x ** arange(1, 5)))
>> c is just the normalizing constant. You will have to find the lagrange
>> parameters that satisfy the mean, variance, skewness and kurtosis.
>> Sampling from this distribution will be tricky, though. You will have
>> to resort to general methods that are going to be pretty slow.
> This is of course a very good suggestion. However, mind that this
> claim is only true if the support of your desired distribution is the
> entire real axis. I recall that I once tried to find the maximum
> entropy distribution with given mean and variance, but such that the
> support was the positive reals (including 0), rather then the entire
> real line. This was less easy then I initially thought, and it is
> certainly not the normal distribution.
Right. Since he was starting with the normal distribution, I assume he
wanted something that went over the entire real axis.
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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