Revision as of 16:07, 18 November 2008 by Kpesyna (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-E[X]\right|\geq \alpha)\leq\frac{var(X)}{\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

Ph.D. on Applied Mathematics in Aug 2007. Involved on applications of image super-resolution to electron microscopy

Francisco Blanco-Silva