Hello,

I think that the best method available for this application (you wil
need a massive number of draws) is the rejection method
(https://en.wikipedia.org/wiki/Rejection_sampling). Let us continue this
discussion on Scilab's Discourse...

S.

On 4/29/24 05:11, Heinz Nabielek wrote:
Colleagues:

bird flight altitude probabilities are given by the exponentially modified 
Gaussian distribution EMG f=f(h), in my case

f = (a/2)*exp((a/2)*((a*σ^2)-2*h)) .* erfc((a*σ^2-h)/σ/sqrt(2))

with h=hmeasured-85 in meters, a=1/60m, σ=30m. See: 
<https://antiphishing.vadesecure.com/v4?f=TzBPM05TMWhaUkVuRncwepXXm7aQUUs8a3sBKnOeo3zqzzPE1PUeJxFSe39wh75Y&i=NmhwUWdPbjljNWRxSlVxNUTscW_rXmsdS5CfW3ms-T8&k=Yhxs&r=bGlzcjBDTTd4VWcyWjZtQ6_TAL2evagZ2vYr-1xJf13LwHIc8EKwrvfWFnwxUdMu&s=54fcca2dabb8b02e9b10abb7c1f2938e90f160a2cb53c8e34c8bf8d215ab3a96&u=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FExponentially_modified_Gaussian_distribution>.

For bird flight Monte-Carlo simulations, I need random deviates representing 
this distribution.
Normally, I generate the cumulative function F of f. Then, I project uniform 
random numbers U(0,1) on F^-1, the inverse of the cumulative.

With the EMG, closed solutions are not available and I need a numerial 
procedure to generate EMG deviates.
What is the best way to do this?
Best greetings
Heinz
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