Line 2: Line 2:
 
* FX(x) = P(X <= x) = integral(-inf to inf) fX(y) dy
 
* FX(x) = P(X <= x) = integral(-inf to inf) fX(y) dy
 
* 1 - FX(x) = P(X > x)
 
* 1 - FX(x) = P(X > x)
* lim<sub>x<math>-> -\infty</math></sub> f<sub>X</sub>(x) = 0 lim<sub>x-> inf</sub> f<sub>X</sub>(x) = 0
+
 +
  lim<sub>x-> -inf</sub> F<sub>X</sub>(x) = 0
 +
  lim<sub>x-> inf</sub> F<sub>X</sub>(x) = 1
  
  

Revision as of 10:51, 21 October 2008

Cumulative Density Function (CDF)

  • FX(x) = P(X <= x) = integral(-inf to inf) fX(y) dy
  • 1 - FX(x) = P(X > x)
 limx-> -inf FX(x) = 0
 limx-> inf FX(x) = 1


Exponential RV

PDF: fX(x) = $ \lambda*e^{-\lambda*x} $, x >= 0 ; fX(x) = 0 , else

CDF: FX(x) = $ 1-e^{-\lambda*x} $

  • E[X] = 1/$ \lambda $ , var(X) = 1/($ \lambda)^2 $


PDF Properties

  • $ f_X(x)\geq 0 $ for all x
  • $ \int\limits_{-\infty}^{\infty}f_X(x)dx = 1 $
  • If $ \delta $ is very small, then
 $  P([x,x+\delta]) \approx f_X(x)\cdot\delta $
  • For any subset B of the real line,
 $  P(X\in B) = \int\limits_Bf_X(x)dx  $

Alumni Liaison

Correspondence Chess Grandmaster and Purdue Alumni

Prof. Dan Fleetwood