Normal/Gaussian Function or Method: Normal Probability Density

#1 Опубликовано: 2 года назад

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This matrix generator (thanks gUrrozEN) produces random 'equal-probability' values for a given center value and tolerance band.

I'm hoping to revise this to return values within a normal/Gaussian probability as illustrated:

Random to Normal.sm (42 КиБ) скачан 82 раз(а).

Any thoughts?

#2 Опубликовано: 2 года назад

Jean Giraud

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Any thoughts ?

Native Smath plugins generate uniform randoms.
The Central Theorem proves that enough uniform randoms
generate a Normal Distribution.
You simply need to bin in an histogram utility.
Is that is what you are looking for ?
Otherwise, I don't understand your request.
Cheers ... Jean.

#3 Опубликовано: 2 года назад

Mark R Harris

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Thank you for the reply Jean.

I'm after a method or function that will return a random value with the probability that 68.2% of the time it will be within 1 sigma, and 95.4% of the time it will be within 2 sigmas, etc.

#4 Опубликовано: 2 года назад

Alvaro Diaz Falconi

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Hi. For 'convert' random uniform distributed numbers between 0 and 1 to another probability distribution, in your case, for get some normal distributed numbers you can use the inverses of the cdf distributions. You can check that here for 1000 numbers you have the desired mean and std deviation

so, I guess that in your function rmat this is the function for normal distributed numbers

random to nomral.sm (5 КиБ) скачан 95 раз(а).

Best regards.
Alvaro.

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Mark R Harris 2 года назад

#5 Опубликовано: 2 года назад

Mark R Harris

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Thanks Alvaro, very greatly appreciated.

For me, the sample file has a problem:

#6 Опубликовано: 2 года назад

Alvaro Diaz Falconi

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Hi. I mean: substitute that M[i,j] into your rmat(...) function for get normal distributed numbers with the rmat algorithm instad uniform distributed ones.

Best regards.
Alvaro.

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Mark R Harris 2 года назад

#7 Опубликовано: 2 года назад

Jean Giraud

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Thank you for the reply Jean.

I'm after a method or function that will return a random value with the probability that 68.2% of the time it will be within 1 sigma, and 95.4% of the time it will be within 2 sigmas, etc.

I don't understand completely ... sorry.
However, from a native random sigma, you can solve for a desired other one.
Cheers, be good Down Under ... Jean.