Le 03/05/2018 à 15:49, Heinz Nabielek a écrit :
Many thanks.
<https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/en.wikipedia.org/wiki/Autoregressive_model#Example:_An_AR(1)_process>
works fine on an originating normal distribution [where i would not need it, because we have
grand(n, "mn", Mean, Cov) in SciLab].
grand(n, "mn", Mean, Cov) allows to generate realizations of a
multi-normal variable, i.e. covariance matrix describes the covariance
between each component of the (vector) variable.
AR processes are different stuff (random process <> random variable).
For an AR process the non-idependance is w.r.t. to time (discrete or
continuous)
Your wind data is a random process.
With an initiating U(-0.5, 0,5), however, I get a triangle function, but not a
autocorrelated uniform random distribution.
what do you mean by " autocorrelated uniform random distribution" ? Here
your triangle is just the distribution of a sum of two uniform variables.
S.
What to do?
Heinz
On 03.05.2018, at 11:37, Stéphane Mottelet <stephane.motte...@utc.fr> wrote:
Why don't start with a simple autoregressive process ? See e.g.
https://antispam.utc.fr/proxy/2/c3RlcGhhbmUubW90dGVsZXRAdXRjLmZy/en.wikipedia.org/wiki/Autoregressive_model
Le 03/05/2018 à 11:15, Heinz Nabielek a écrit :
Uniform U(0,1) random deviates with given autocorrelation would be good
enough…...
On 03.05.2018, at 10:57, Heinz Nabielek <heinznabie...@me.com> wrote:
Friends:
urgently needed: a SciLab routine for generating Weibull random variables with
prior given autocorrelation.
Uncorrelated is easy with
vc*((-log(grand(1,5,"def"))).^(1/m)) producing
0.9666848 1.4646143 10.402793 5.9513675 3.3241823
with characteristic velocity vc=7 and modulus m=2.
But I need Weibull deviates with given autocorrelation, say rho=0.80
Literature has dozen of references on the subject which either do not work or I
do not know how to code.
Any help here?
Heinz
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