When you convolve a probability distribution with itself, it converges towards a Guassian. When the distribution is constrained to only have non-zero probability density in the positive half, it looks more like the first derivative of the Gaussian. (Take a trncated Gaussian and convolve it with itself in your mind's eye.)
Your second sentence contradicts the first, no? The central limit theorem applies to any distribution.
[I admit that I'm awkward with the style of language you're using here, but at least that's what I get when I look at what you've written...]
After some coffee I'll rephrase that, if I can make those who read it not lose IQ points. *sigh* It's been a day.
Yes, it has. I hate teaching about finite automata... |