Central limit theorem
Central limit theorems are a set of weak-convergence results in probability theory. Intuitively, they all express the fact that any sum of many independent identically distributed random variables will tend to be distributed according to a particular "attractor distribution". The most important and famous result is simply called The Central Limit Theorem which states that if the summed variables have a finite variance then they will be approximately normally distributed. Since many real processes yield distributions with finite variance, this explains the ubiquity of the normal distribution.
External links
- Animated examples of the CLT
- Central Limit Theorem Java
- Central Limit Theorem interactive simulation to experiment with various parameters
~ Table of Content ~
| ► | Introduction |
| ► | Classical central limit theorem |
| ► | Lyapunov condition |
| ► | Lindeberg condition |
| ► | Non-independent case |
| ► | External links |
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