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Re: Re: Normal distribtion

  • To: mathgroup at
  • Subject: [mg49226] Re: [mg49201] Re: Normal distribtion
  • From: DrBob <drbob at>
  • Date: Thu, 8 Jul 2004 02:51:01 -0400 (EDT)
  • References: <> <> <ccdlms$sd5$> <>
  • Sender: owner-wri-mathgroup at

Did you test that code?

Sin is often negative, hence x[Sin[....]] is Complex. Listplot, Max, Min, and Floor can't handle complex data, so there are LOTS of error messages.


On Wed, 7 Jul 2004 01:42:40 -0400 (EDT), Roger L. Bagula <rlbtftn at> wrote:

> To give a way of showing the symmetrical distribution
> I worked this program up.
> I use the Sign[2*Random[]-1] trick to get an equal sign distribution
> so that both sides of the distribution which is even can be seen.
> Since this distribution is closer to single valued on the real line
> even a 10000 it is very noisy.
> An equalized distribution with count:
> x[a_]=(1+Sqrt[a])/a
> noise=Table[Sign[2*Random[]-1]*Exp[-x[Sin[2*Pi*(2*Random[]-1)]]^2/2]/Sqrt[2*Pi],{n,1,
>        10000}];
> ListPlot[noise,PlotRange->All, PlotJoined-> True]
> b=Table[Floor[2500*noise[[n]]],{n,1,10000}];
> b0=Dimensions[b][[1]]
> bmax=Max[b]
> bmin=Min[b]
> c=Table[Count[b,n],{n,Floor[bmin],bmax}];
> ListPlot[c,PlotJoined->True,PlotRange->All]
> Roger L. Bagula wrote:
>> I found a better faster way to get a Gaussian/ white noise:
>> In Mathematica notebook style:
>> x[a_]=(1+Sqrt[1-a^2))/a
>> Noise=Table[Exp[-x[Sin[2*Pi*Random[]]]^2/2/Sqrt[2*Pi],{n,1,500}]
>> ListPlot[noise,PlotRange--> All,PlotJoined->True]
>> It is a projective line ( circle to line random taken as the basic for a
>> normal distribution's amplitude.) based algorithm.
>> Since it doesn't use either the polar method with has a choice loop or
>> the older 12 random method, the random operation has only to be used
>> once for each noise value.
>> It is faster by several factors and can be translated to almost any
>> language.
>> George Marsaglia wrote:
>>> "philou" <philou2000 at> wrote in message
>>> news:7228735a.0407050100.4695fc68 at
>>>> Hi,
>>>> I heard that it was possible to get a realisation of a normal
>>>> distribution from two realisations of an uniform distribution. Can
>>>> someone explain me how to do that ? What transformations should I do ?
>>> Your hazy reference may have been based on my polar method for
>>> generating a pair of independent standard normal variates X and Y:
>>> If U and V are independent uniform in (-1,1), conditioned by
>>>               S = U^2+V^2 < 1
>>> then S is uniform in (0,1) and independent of the point
>>> (U/sqrt(S),V/sqrt(S)), which is uniform on the unit circumference.
>>> Thus if R=sqrt(-2*ln(S)/S) then
>>>      X=R*U
>>>      Y=R*V
>>> are a pair independent standard normal variates, obtained by
>>> projecting that uniform point on the unit circumference
>>> a random distance with a root-chi-square-2 distribution,
>>> exploiting the uniformity of S and its independence of
>>> the random point on the circumference.
>>> Of course one has to discard uniform (-1,1) pairs U,V
>>> for which S=U^2+V^2>1, so each normal variate is produced
>>> at an average cost of 4/pi=1.27 uniform variates.
>>> This method is sometimes improperly attributed to Box and Muller,
>>> who pointed out that pairs of normal variates could be generated as
>>> rho*cos(theta), rho*sin(theta) with rho root-chisquare-2 , sqrt(-2*ln(U)),
>>> and theta uniform in (0,2pi), a result we owe to Laplace, who showed us
>>> how to find  the infinite integral of exp(-x^2) by getting its square
>>> as the integral of exp(-x^2-y^2), then transforming to polar coordinates.
>>> For a method faster than my polar method, requiring about 1.01 uniform
>>> variates per normal variate, try the ziggurat method of
>>> Marsaglia and Tsang, in volume 5, Journal of Statistical Software:
>>> George Marsaglia

DrBob at

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