Revision as of 16:06, 18 November 2008 by Nbrowdue (Talk)

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-\mu\right|\geq \alpha)\leq\frac{\sigma^2}{\alpha^2}. $

ML Estimation Rule

MAP Estimation Rule

Bias of an Estimator, and Unbiased estimators

Confidence Intervals, and how to get them via Chebyshev

Alumni Liaison

Recent Math PhD now doing a post-doctorate at UC Riverside.

Kuei-Nuan Lin