note
gryng
Oh, definitively [tilly].
<p> I thought we were talking about Monte-Carlo integration (sorry, I did
say Monte-Carlo searching). But yes, for -some- Monte-Carlo searches
uniform distribution and non repetitive bias would be <b>bad</b> things!
<p> I think it is important to point out, like you did, that the real crux
of the matter is to understand what kind of random numbers you want and
why you want them.
<p> Let's assume we're sticking to uniform distributions of some type and
do a quick summary of which ones we've discussed so far (to any that have
actually followed this discussion this far! lol :) ):
<p> One: Truely-random numbers. In this case we are talking about a true
random source, that <b>should</b> be uniform, but we do not get any
garuntees about it. This is almost always an all around safe bet if you
can't decide. Also in some very sensitive conditions, this is the only
bet. E.g. the chaotic systems [tilly] from above mentions, for example
the [RoShamBo|http://www.cs.ualberta.ca/~darse/rsbpc.html]. However for
Monte-Carlo integration these converge, but generally at 1/N**2 rate.
<p> Two: Pseudo-random numbers. These are normally meant to be uniformly
distributed (using statistical garuntees), but in practice one finds
otherwise. These numbers should not generally not be used for security
unless you know what you are doing. The reason being that pseudo-random
numbers are predictable if you know or can guess the seed and the general
algorithm. For general purpose though, these are the best, because they
are fast and provide what many programs need. For Monte-Carlo integration
they <b>should</b> converge, but because of bad implementations they often
won't.
<p> Three: Quasi-random numbers. These are sequences that are garunteed
to be uniform statistically, and also have a strong bias to not repeating
themselves. This means that as you pick more numbers the become closer
and closer together, but in a uniform way. Example is the Hamilton
sequence mentioned in the posts above. These are excellent for
Monte-Carlo integration because they lead to a 1/N convergance rate and
are garunteed to converge. These numbers tend to be very predictable, so
they should probably not be used in security for the same reasons as
Pseudo-random.
<p> Welp, back to work :)
<p>Ciao,
<br>Gryn
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