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==ML Estimation Rule== | ==ML Estimation Rule== | ||
| − | <math>\hat a_{ML} = \text{max}_a ( f_{X}(x_i;a) )</math> | + | <math>\hat a_{ML} = \text{max}_a ( f_{X}(x_i;a) \text{For continuous} )</math> |
| + | |||
| + | <math>\hat a_{ML} = \text{max}_a ( Pr(x_i;a) \text{For discrete} )</math> | ||
==MAP Estimation Rule== | ==MAP Estimation Rule== | ||
Revision as of 17:22, 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
"Any RV is likely to be close to its mean"
- $ \Pr(\left|X-E[X]\right|\geq C)\leq\frac{var(X)}{C^2}. $
ML Estimation Rule
$ \hat a_{ML} = \text{max}_a ( f_{X}(x_i;a) \text{For continuous} ) $
$ \hat a_{ML} = \text{max}_a ( Pr(x_i;a) \text{For discrete} ) $
