Line 1: Line 1:
 
[[Category:ECE600]]
 
[[Category:ECE600]]
 
[[Category:Lecture notes]]
 
[[Category:Lecture notes]]
 +
[[ECE600_F13_notes_mhossain|Back to all ECE 600 notes]]
 +
  
 
<center><font size= 4>
 
<center><font size= 4>
Line 6: Line 8:
 
</font size>
 
</font size>
  
<font size= 3> Topic 12: Random Variables: Distributions</font size>
+
<font size= 3> Topic 12: Independent Random Variables</font size>
 
</center>
 
</center>
  

Revision as of 13:57, 12 November 2013

Back to all ECE 600 notes


Random Variables and Signals

Topic 12: Independent Random Variables



We have previously defined statistical independence of two events A and b in F. We will now use that definition to define independence of random variables X and y.

Definition $ \qquad $ Two random variables X and Y on (S,F,P) are statistically independent if the events {X ∈ A}, and {Y ∈ B} are independent ∀A,B ∈ F. i.e.

$ P(\{X\in A\}\cap\{Y\in B\})=P(X\in A)P(Y\in B) \quad\forall A,B\in\mathcal F $

There is an alternative definition of independence for random variables that is often used. We will show that X and Y are independent iff

$ f_{XY}(x,y)=f_X(x)f_Y(y)\quad\forall x,y\in\mathbb R $


First assume that X and Y are independent and let A = (-∞,x], B = (-∞,y]. Then,

$ \begin{align} F_{XY}(x,y) &= P(X\leq x,Y\leq y) \\ &= P(X\in A,Y\in B) \\ &= P(X\in A)P(Y\in B) \\ &= P(X\leq x)P(Y\leq y) \\ &= F_X(x)F_Y(y) \\ \Rightarrow f_{XY}(x,y) &= f_X(x)f_Y(y) \end{align} $

Now assume that f$ _{XY} $(x,y) = f$ _X $(x)f$ _Y $(y) ∀x,y ∈ R. Then, for any A,B ∈ B(R)

$ \begin{align} P(X\in A,Y\in B) &= \int_A\int_Bf_{XY}(x,y)dydx \\ &=\int_A\int_Bf_X(x)f_Y(y)dydx \\ &=\int_Af_X(x)dx\int_Bf_Y(y)dy \\ &= P(X\in A)P(Y\in B) \end{align} $

Thus, X and Y are inedependent iff f$ _{XY} $(x,y) = f$ _X $f)X$ _Y $.



References



Questions and comments

If you have any questions, comments, etc. please post them on this page



Back to all ECE 600 notes

Alumni Liaison

Correspondence Chess Grandmaster and Purdue Alumni

Prof. Dan Fleetwood