(→Chebyshev Inequality) |
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==Chebyshev Inequality== | ==Chebyshev Inequality== | ||
| − | :<math>\Pr(\left|X- | + | :<math>\Pr(\left|X-E[X]\right|\geq \alpha)\leq\frac{var(X)}{\alpha^2}.</math> |
==ML Estimation Rule== | ==ML Estimation Rule== | ||
Revision as of 17:07, 18 November 2008
Contents
Covariance
- $ COV(X,Y)=E[(X-E[X])(Y-E[Y])]\! $
- $ COV(X,Y)=E[XY]-E[X]E[Y]\! $
Correlation Coefficient
$ \rho(X,Y)= \frac {cov(X,Y)}{\sqrt{var(X)} \sqrt{var(Y)}} \, $
Markov Inequality
Loosely speaking: In a nonnegative RV has a small mean, then the probability that it takes a large value must also be small.
- $ P(X \geq a) \leq E[X]/a\! $
for all a > 0
Chebyshev Inequality
- $ \Pr(\left|X-E[X]\right|\geq \alpha)\leq\frac{var(X)}{\alpha^2}. $
