Difference between revisions of "Cheat3 ECE302Fall2008sanghavi" - Rhea

Revision as of 17:36, 18 November 2008

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

EXAMPLE:

On average it takes 1 hour to catch a fish. What is (an upper bound) the probability it will take 3 hours?

SOLUTION:

Using Markov's inequality, where E[X] = 1 and a = 3.

$ P(X \geq 3) \leq \frac {E[X]}{3} = \frac{1}{3} $

so 1/3 is the upper bound to the probability that it will take more than 3 hours to catch a fish.

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}. $

Weak Law of Large Numbers

The weak law of large numbers states that the sample average converges in probability towards the expected value

$ \overline{X}_n \, \xrightarrow{P} \, \mu \qquad\textrm{for}\qquad n \to \infty. $

ML Estimation Rule

$ \hat a_{ML} = \text{max}_a ( f_{X}(x_i;a)) $ continuous

$ \hat a_{ML} = \text{max}_a ( Pr(x_i;a)) $ discrete

If X is a binomial (n,p), where is X is number of heads n tosses, Then, for any fixed k-value;

$ \hat p_{ML}(k) = k/n $

MAP Estimation Rule

$ \hat \theta_{MAP} = \text{argmax}_\theta ( f_{\theta|X}(\theta|x)) $

Which can be expanded and turned into the following (if I am not mistaken):

$ \hat \theta_{MAP} = \text{argmax}_\theta ( f_{X|\theta}(x|\theta)f_{\theta}(\theta)) $

Bias of an Estimator, and Unbiased estimators

An estimator is unbiased if: $ E[\hat a_{ML}] = a $ for all values of a

Confidence Intervals, and how to get them via Chebyshev

$ \theta \text{ is unknown and fixed} $

$ \hat \theta \text{ is random and should be close to } \theta \text{ most of the time} $

$ if Pr[|\hat \theta \text{-} \theta|] <= (1-a) \text { then we say we have (1-a) confidence in the interval } [\hat \theta - E, \hat \theta + E] $