I was surprised that the squaring gave you any kind of banding since it
should be a smooth bias.
I think that the log transformation will give you less of a bias toward
one side but I have not pulled out all my old stats and calculus books
to check this out.
It would seem that a normal distribution(cut in half and shifted) or a
Poisson might be what you are looking for.
What is the physical phenomenon are you trying to model?
http://mathworld.wolfram.com/topics/ContinuousDistributions.html has
more distributions that I ever knew existed.
It has a picture and formula for each one.
Ron
clark slater wrote:
Hi Ron,
Thanks for jumping into this thread amongst the tumbleweeds. Yes, I
suspect
something logarithmic could work but don't know where to start. I am
trying
to distribute 1000 particles randomly in a rectangular distribution area,
with a bias towards one side of the rectangle.
Clark
On 5/26/06, Ron Wheeler <[EMAIL PROTECTED]> wrote:
Would a formula based on logarithms give you what you want as well?
You were not very specific about what you wanted the distribution to
look like.
Ron
clark slater wrote:
> Here I go answering my own question.
>
> To make it more dense toward the origin, use {x,y} = {random[]^2,
> random[]^2}--squaring the independently generated numbers
> (random[]*random[]
> won't work). The result is a distribution with a sharp cusp at the
> origin--the probability is a maximum there, and has a discontinuous
> derivative. You can use higher powers for narrower (and sharper)
> distributions.
>
> Damn useful I say!
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