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  1. #1
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    Cool probability and chaos

    I have some thoughts about probability theory.
    Lets assume two balls , ball 1 and ball 2.
    We draw the one ball every time, and the starting conditions are the same every time - two balls. There is pure chaos during the draw, and we can draw infinite number of times.
    We will surely draw both balls, with this starting conditions, we wont draw only ball 1 every time until infinity, or vice versa.For example : first draw-1....10 th draw-1.....but surely after some time there will be - X-th draw- 2.

    Now, assume a lottery 6/90.There is about 6,2*10^8 combinations. My guess is that every combination will appear infinite number of times if we can make infinite number of draws and if there is a pure chaos during the draw.
    Then, only thing that matters is that the system from which we draw is finite.
    No matter how big it is, for example :
    hyper - lottery 10นบบบบบบบบบบบบบบ /10นบบบบบบบบบบบบบบบบบบบบบบบบบบบบบบบบ each combination will repeat infinite number of times if we can make infinite number of draws and if there is a pure chaos during the draw.

    Is this right analogy?

    If not, please correct me.
    Thanks!

    Marko

  2. #2
    The Thinker
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    It is a correct mathematical analogy. If and only if you concieve both examples as sequences of independent events. That is, how I draw the first ball or play the first lottery, has nothing to do with the following one nor odes it affect it's probability functions. And this I think is what you meant by 'pure disorder and chaos' so yes it's correct. Where do you want to go? What do you want to achieve to explain with this? I'm curious, I'm sure it'll be something interesting.

  3. #3
    Raider of the lost time
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    probability

    probability of the past, probability of the present, probability of the future. Which of these is the one used by chaos theory?
    Time independence: [∂E(g)]ฒ=[∂F(a)ื∂r(a)][∂F(b)ื∂r(b)] and Mass independence: a(t)ทr(t)=c

  4. #4
    The Thinker
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    Quote Originally Posted by AntonioLao
    probability of the past, probability of the present, probability of the future. Which of these is the one used by chaos theory?
    Chaos theory uses probability of the present towards to future. That is, it states the many possibilities that there are NOW (present) for what can happen, which in any case will happen in THEN (future).

  5. #5
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    Smile both are just ideas thats all.

    there is a strong probality that chaos theory is all contained within the basic
    Idea,of,thoughtfulness made manifest,and all these are just concepts within
    your mind and mine,we keep going around in circles,a kind of mental masturbation,where the climax is somewhat delayed by another bright spark with
    a new idea of what reality is.




    kind regards michael.
    Humilty,coupled with boldness,surprises truth to
    reveal herself?

  6. #6
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    past too

    Quote Originally Posted by GUILLE
    Chaos theory uses probability of the present towards to future.
    But all the probability distribution functions (for example the normal distribution and by the central limit theorem and law of large number says that all distribution eventually tend to the normal) must be based on past data.
    Time independence: [∂E(g)]ฒ=[∂F(a)ื∂r(a)][∂F(b)ื∂r(b)] and Mass independence: a(t)ทr(t)=c

  7. #7
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    Quote Originally Posted by AntonioLao
    But all the probability distribution functions (for example the normal distribution and by the central limit theorem and law of large number says that all distribution eventually tend to the normal) must be based on past data.
    Yeah. However, the main difference between clasical probability and modern probability (chaos, etz), is that the previous based the past data for present events, whiles the later based on past data to perform an investigation of the future events that can be done in the present. It's a progress, I believe.

  8. #8
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    Quote Originally Posted by GUILLE
    It's a progress, I believe.
    It is more like a Markov chain reaction.
    Time independence: [∂E(g)]ฒ=[∂F(a)ื∂r(a)][∂F(b)ื∂r(b)] and Mass independence: a(t)ทr(t)=c

 

 

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