Chain-of-Thought
Artificial Intelligence
A fundamental principle stating that the sum of many independent random variables tends toward a normal distribution.
Detailed Explanation
The Central Limit Theorem (CLT) is a statistical principle stating that when independent random variables are summed, their normalized total approaches a normal distribution, regardless of the original variables' distributions. In AI, this theorem underpins statistical inference and helps approximate complex data behaviors, enabling better model training, error estimation, and decision-making processes.
Use Cases
- •Use case: Estimating model errors and confidence intervals in AI training processes using the normal approximation.
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