Probability theory is mainly extracted from the equation a
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1. If X is a one-dimensional continuous random variable, and Y = g (X) is a monotonically X function , then let h (y) of Y = g (X) is the inverse function of:
2 continuous random variable function of mathematical expectation:
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3. The two-dimensional random variable function of mathematical expectation:
4. independent random variables:
The conditional distribution function and the relationship between density functions conditions:
6. The two continuous random variable function distribution:
An important formula:
Finite number independently a linear combination of normal random variables remained normal distribution.
7. variance:
Chebyshev's inequality gives a distribution of an unknown random variable , but only know E (X) and D (X) in the case of the method for estimating the probability bounds {P | <| XE (X-)} .
8. covariance and correlation coefficient:
