randall at randallreetz.com
Thu Nov 13 12:35:41 CST 2008
Thank you Francois,
Can statistics be rigorously derived from proability math? I hope so. Both are heavily dependent on what appears to be statistics ("both" refers here to my twin gods). I am a self admitted thermodynamics and information science freak. I'd hate to think that my whole world was anecdotally argued. I do see a strange but familiar symmetry between the finite/infinite distinction that seperates probability theory and practice, and the open/closed system maths that seperates the thermodynamic engineering from pure science.
From: "Eric Chatonet" <eric.chatonet at sosmartsoftware.com>
To: "How to use Revolution" <use-revolution at lists.runrev.com>
Sent: 11/13/2008 9:51 AM
Subject: Re: Random algorithm
Great post indeed :-)
I fully agree.
Le 13 nov. 08 à 18:47, François Chaplais a écrit :
> Le 13 nov. 08 à 03:39, Randall Reetz a écrit :
>> And another problem is that a random and unique solution actually
>> reduces randomness as it is run. Each time you eliminate a
>> number, the set of numbers left is reduced. This is even true of
>> an infinate number randomizer. Sometimes i wonder if this
>> fascination with random number generation isnt a good diagnosis of
>> severe case of the geeks.
> maybe it is just a lack of mathematical background
>> -----Original Message-----
>> From: "Randall Reetz" <randall at randallreetz.com>
>> To: "How to use Revolution" <use-revolution at lists.runrev.com>
>> Sent: 11/12/2008 6:18 PM
>> Subject: RE: Random algorithm
>> There is a huge difference between random and unique. If you are
>> after unique then just use the counting numbers. If you need both
>> random and unique you will have to check each number generated
>> against a saved list of every previous number. There is nothing
>> wrong with a random number generator that spits out duplicate
>> numbers. Random is blind to history (and future). Random is not
>> nostalgic. A coin with two sides is just as good at random as a
>> pair of thousand sided dice.
> actually, random is so little nostalgic that a random sequence of
> zeros and ones (with equal probabilities) can produce ones for a
> zillion consecutive ones without invalidating the probabilistic
> model. This fact holds (mathematically) as long as the number of
> events is finite (which is always the case in practice). The
> central limit theorem only holds for an "actual" infinite number of
> Of course, some may object that having a zillion consecutive ones
> is unprobable; however, this assumption itself can only be verified
> by repeating the experience an actual infinity of times, so we're
> back to the same modelling problem.
> In practice, people do not refer to probabilities but to
> statistics. As far as I know there are two schools of statisticians
> (at least when it comes to teaching)
> 1) the "clean" statisticians present statistics as an offspring of
> probabilities; it is mathematically clean but has the same
> weaknesses when to it comes to confronting the model to the
> 2) the "dirty" statisticians admit that if your random process
> produces a zillion ones, then you have to pull the trigger on the
> model, arguing that modelling the sequence by a constant is closer
> to what happens and as economical as the flawed statistical model.
> A zillion or two zillion limit: you chose.
[truncated by sender]
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