Difference between revisions of "ECE600 F13 Conditional Expectations for Two Random Variables mhossain" - Rhea

Latest revision as of 12:13, 21 May 2014

Topic 16: Conditional Expectation for Two Random Variables

If X and Y are random variables on (S,F,P) and ∈ F with P(M) > 0, then,

E[g(X,Y)|M] = \int\int_{\mathbb R^2} g(x,y)f_{XY}(x,y|M)dxdy

One important case is when M = {Y = y} for some y ∈ R. Then we have that

E[g(X,Y)|Y=y]=\int\int_{\mathbb R^2}g(x,y')f_{XY}(x,y'|Y=y)dxdy'

Using our old trick, let

E[g(X,Y)|Y=y]=\lim_{\Delta y\rightarrow 0}\int\int_{\mathbb R^2}g(x,y')f_{XY}(x,y'|y<Y\leq y+\Delta y)dxdy'

Using this approach, it can be shown that

\begin{align} E[g(X,Y)|Y=y]&=E[g(X,y)|Y=y] \\ &=\int_{-\infty}^{\infty}g(x,y)f_{X|Y}(x|y)dx \\ \end{align}

Another important case: g(X,Y)=g(X)

\begin{align} E[g(X)|M]&=\int\int g(x)f_{XY}(x,y|M)dxdy \\ &=\int g(x)\left[\int f_{XY}(x,y|M)dy\right]dx \\ &=\int g(x)f_X(x|M)dx \end{align}

Note that this is the same equation we had, for example

E[g(X)|Y=y]=\int g(x)f_{X|Y}(x|y)dx

Iterated Expectation

Sometimes we want to work with f $_{Y|X}$ (y|x) and f $$ _X $$ (x) instead of f $_{XY}$ (x,y). This can make computation of E[g(X,Y)] easier in some cases. We can write

\begin{align} E[g(X,Y)]&=\int\int_{\mathbb R^2}g(x,y)f_{XY}(x,y)dxdy \\ &=\int\int_{\mathbb R^2}f_X(x)g(x,y)f_{Y|X}(y|x)dydx \\ &=\int_{\mathbb R}f_X(x)E[g(X,Y)|X=x]dx \end{align}

Note that E[g(X,Y)|X=x] is a function of x ∈ R. We will call this function h.

h(x)=E[g(X,Y)|X=x]\qquad h:\mathbb R\rightarrow\mathbb R

We can create a random variable h(X). We will use the notation

E[g(X,Y)|X]\equiv h(X)

So we have

h(x) = E[g(X,Y)|X=x] \

which is a real-valued function of x ∈ R, and h(X), which is a random variable since it is a function of random variable X.

Now we can write

\begin{align} E[g(X,Y)]&=\int_{\mathbb R}E[g(X,Y)|X=x]f_X(x)dx \\ &= \int_{\mathbb R}h(x)f_X(x)dx \\ &=E[h(X)] \end{align}

So,

E[g(X,Y)] = E[E[g(X,Y)|X]] \

We call this iterated expectation.

An important special case is when g(X,Y)=Y, in which case, we have

E[Y]=E[E[Y|X]] \

Example $\qquad$ Suppose we have a stick of length l. We break the stick at a uniformly chosen point Y, then again at a uniformly chosen point X. Find E[X].

Fig 1: example problem

\begin{align} E[X]&=\int\int_{\mathbb R^2}xf_{XY}(x,y)dxdy \\ &=\int_{\mathbb R}xf_X(x)dx \end{align}

We do not know f $_{XY}$ or f $$ _X $$ , but we know f $_{X|Y}$ or f $$ _Y $$

\begin{align} f_Y(y)&=\frac{1}{l}\qquad\ &0\leq y\leq l \\ f_{X|Y}(x|y)&=\frac{1}{y}\qquad &0\leq x\leq y \end{align}

Use E[X]=E[E[X|Y]]. Now

h(y)\equiv E[X|Y=y] = \frac{y}{2}

since X is uniform on [0,y] given Y=y. So,

h(Y)=\frac{Y}{2}

Then

E[X]=E\left[\frac{Y}{2}\right]=\frac{1}{2}E[Y]=\frac{1}{2}.\frac{l}{2}=\frac{l}{4}

References

M. Comer. ECE 600. Class Lecture. Random Variables and Signals. Faculty of Electrical Engineering, Purdue University. Fall 2013.

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Back to all ECE 600 notes

@@ Line 3: / Line 3: @@
 [[ECE600_F13_notes_mhossain|Back to all ECE 600 notes]]
+[[Category:ECE600]]
+[[Category:probability]]
+[[Category:lecture notes]]
+[[Category:slecture]]
 <center><font size= 4>
-'''Random Variables and Signals'''
+[[ECE600_F13_notes_mhossain|'''The Comer Lectures on Random Variables and Signals''']]
 </font size>
+[https://www.projectrhea.org/learning/slectures.php Slectures] by [[user:Mhossain | Maliha Hossain]]
 <font size= 3> Topic 16: Conditional Expectation for Two Random Variables</font size>
 </center>
 ----
+----
-If X and Y are random variables on (''S,F'',P) and  ∈ ''F'' with P(MP > 0, then, <br/>
+If X and Y are random variables on (''S,F'',P) and  ∈ ''F'' with P(M) > 0, then, <br/>
 <center><math>E[g(X,Y)|M] = \int\int_{\mathbb R^2} g(x,y)f_{XY}(x,y|M)dxdy</math></center>
@@ Line 45: / Line 52: @@
 ==Iterated Expectation==
-Sometimes we want to work with f<math>_{Y|X}</math>(y|x) and f<math>_X,/math>(x) instead of f<math>_{XY}</math>(x,y). This can make computation of E[g(X,Y)] easier in some cases. We can write <br/>
+Sometimes we want to work with f<math>_{Y|X}</math>(y|x) and f<math>_X</math>(x) instead of f<math>_{XY}</math>(x,y). This can make computation of E[g(X,Y)] easier in some cases. We can write <br/>
 <center><math>\begin{align}
 E[g(X,Y)]&=\int\int_{\mathbb R^2}g(x,y)f_{XY}(x,y)dxdy \\
 &=\int\int_{\mathbb R^2}f_X(x)g(x,y)f_{Y|X}(y|x)dydx \\
-&=\int_{\mathbb R}f|X(x)E[g(X,Y)|X=x]dx
+&=\int_{\mathbb R}f_X(x)E[g(X,Y)|X=x]dx
 \end{align}</math></center>
@@ Line 66: / Line 73: @@
 E[g(X,Y)]&=\int_{\mathbb R}E[g(X,Y)|X=x]f_X(x)dx \\
 &= \int_{\mathbb R}h(x)f_X(x)dx \\
-&=h(x)
+&=E[h(X)]
 \end{align}</math></center>
@@ Line 81: / Line 88: @@
-<center>[[Image:fig1_conditional_expectation_of_two_rvs.png|450px|thumb|left|Fig 1: example problem]]</center>
+<center>[[Image:fig1_conditional_expectation_of_two_rvs.png|350px|thumb|left|Fig 1: example problem]]</center>
 <center><math>\begin{align}
-E[X]&=\int\int_{\mathbb R^2}x_f{XY}(x,y)dxdy \\
+E[X]&=\int\int_{\mathbb R^2}xf_{XY}(x,y)dxdy \\
 &=\int_{\mathbb R}xf_X(x)dx
 \end{align}</math></center>
-We do not know f<math>_{XY}</math> or f_<math>_X</math>, but we know f<math>_{X|Y}</math> or f_<math>_Y</math><br/>
+We do not know f<math>_{XY}</math> or f<math>_X</math>, but we know f<math>_{X|Y}</math> or f<math>_Y</math><br/>
 <center><math>\begin{align}
 f_Y(y)&=\frac{1}{l}\qquad\ &0\leq y\leq l \\
@@ Line 94: / Line 102: @@
 \end{align}</math></center>
-Use E[X}=E[E[X|Y]]. Now <br/>
+Use E[X]=E[E[X|Y]]. Now <br/>
-<center><math>h(h)\equiv E[X|Y=y] = \frac{y}{2}</math></center>
+<center><math>h(y)\equiv E[X|Y=y] = \frac{y}{2}</math></center>
 since X is uniform on [0,y] given Y=y. So, <br/>
 <center><math>h(Y)=\frac{Y}{2}</math></center>

Difference between revisions of "ECE600 F13 Conditional Expectations for Two Random Variables mhossain" - Rhea

Latest revision as of 12:13, 21 May 2014

Iterated Expectation

References

Questions and comments

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