Important Concepts in probability and statistics

1. Big Number Theorem

References: http://zh.wikipedia.org/wiki/%E5%A4%A7%E6%95%B0%E5% AE %9A%E5%BE%8B

This is better understood, but often confused with the central limit theorem.

The larger the number, the mean value is closer to the expected value from the Mean Value Point of View. (here, we need to differentiate the concepts of "average" and "expectation ); the more events occur, the closer the frequency value is to the probability value. It is described in mathematical language.

2. central limit theorem

References: http://zh.wikipedia.org/wiki/%E4%B8%AD%E5%BF%83%E6%9E%81%E9%99%90%E5% AE %9A%E7%90%86

I was interested in this theorem because of a Baidu internship interview. On the Wiki, I respected it as the chief Theorem in probability theory, and I began to respect it.

The central limit theorem is a set of theorem that discusses the limit of random variables and distributions based on normal distribution in probability theory. This theorem is the theoretical basis of mathematical statistics and error analysis. It is pointed out that the sum of a large number of random variables is almost subject to normal distribution conditions.

Mathematical language description:

Independent distribution:

Independent mean: