[Scilab-users] {EXT} Can you suggest a more efficient procedure for generating random variables?

[Heinz Nabielek]

I am happy to write a 1.5 page technical   note. What would we do with it 

Greetings from Heinz

> On 18.03.2019, at 17:27, Claus Futtrup <cfuttrup at gmail.com> wrote:
> 
> Hi there
> 
> Interesting ...
> 
> If this is excellent for Monte-Carlo simulations, I wish that such an example is put into the documentation. Lots of people (who need Monte-Carlo) would then accidentally fall over it and take advantage of it.
> 
> Cheers,
> Claus
> 
>> On 18.03.2019 11:06, Heinz Nabielek wrote:
>> Ingenious. Works with precision. Gigantically fast for a million random deviates. Ideal for Monte-Carlo simulations.
>> 
>> I had never heard of dsearch* before......
>> Thanks a lot
>> Heinz
>> 
>> 
>> * I wished the Scilab help files would be more readable.....
>> 
>>> On 18.03.2019, at 09:50, Dang Ngoc Chan, Christophe <Christophe.Dang at sidel.com> wrote:
>>> 
>>> Hello Heinz,
>>> 
>>>> De : Heinz Nabielek
>>>> Envoyé : dimanche 17 mars 2019 23:50
>>>> 
>>>> I need to generate random deviates x according to a given cumulative
>>>> distribution y that is available only in tabular form.
>>>> [...]
>>>> for i=1:N;
>>>> x=[x find(y>z(i),1)];
>>>> end;
>>>> 
>>>> y is a previously defined table with values monotonically increasing from zero
>>> I guess these are quantiles.
>>> 
>>> I think you can vectorise with something like
>>> 
>>> x = dsearch(z, y)
>>> 
>>> I tried a little bit and it seems to work but I don't know the exact application so...
>>> 
>>> HTH
>>> 
>>> --
>>> Christophe Dang Ngoc Chan
>>> Mechanical calculation engineer
>>> 
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