       Re: Normal distribtion

• To: mathgroup at smc.vnet.net
• Subject: [mg49245] Re: Normal distribtion
• From: koopman at sfu.ca (Ray Koopman)
• Date: Fri, 9 Jul 2004 02:26:15 -0400 (EDT)
• References: <7228735a.0407050100.4695fc68@posting.google.com> <QaednZQbSYcwpnTdRVn-vA@comcast.com> <ccdlms\$sd5\$1@smc.vnet.net> <ccg4n7\$ot0\$1@smc.vnet.net> <ccis0d\$49v\$1@smc.vnet.net>
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```"Roger L. Bagula" <rlbtftn at netscape.net> wrote in message
news:<ccis0d\$49v\$1 at smc.vnet.net>...
> Dear Ray Koopman,
> I'm familar with the Lorentzian distribution also called Cauchy also
> called classically the Witch of Agnesi.
> another of the "peak" distributions?
> I use the Normal function to give my amplitude at the address on the
> real line x :{ x,-Infinity, Infinity}.
> By my experiments this function of mine gives a much larger variability
> than the Mathematica built in White noise function or what you get from
> a Polar normal distribution, but the logic of the derivation is clear:
> 1) a random number is found in [0,1]
> 2) a point on a circle is found
> 3) that point is projected to the real line  at x
> 4) that real line value gives an amplitude of a distribution that is a
> normal distribution.
>
> The result is a Gaussian normal noise.
> I really can't make it any simpler.
> The idea was to develop a Gaussian noise generator whose derivation was
> simple and obvious.

I think my problem is with your terminology. I reserve "normal"
and "Gaussian" for variables whose density is proportional to
Exp[-((x-m)/s)^2/2] for some location constant m and scale constant s.
Variables whose density is close to that I might call "near-normal" or
"normal-like", but the Cauchy distribution is so strongly-peaked and
long-tailed that I would say it is far from normal. Indeed, Cauchy
variables are often used to demonstrate how procedures that work fine
with normal and near-normal variables can give terrible results with
variables whose distributions are long-tailed.

However, even if we accept the Cauchy as near-normal, I guess I also
don't understand what you mean by "noise". I would have thought it
would be y = x[Sin[2*Pi*Random[]]] itself, not f[y], where f is some
probability density function.

```

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