Conditional Expectation

In summary, the joint pdf is f(x,y) = 2 ; 0<x<y<1 ; 0<y<1. To find E(Y|x) and E(X|y), we first need to calculate f(x) by integrating from x to 1, which gives us f(x) = 2(1-x). Then, using f(x), we can calculate f(y|x) = 1/(1-x). Finally, using the equations E(Y|x) = \int Y*f(y|x)dy and E(X|y) = \int X*f(x|y)dx, we can calculate that E(Y|x) = (1+x)/2 and E(X|y) = (1+y
  • #1
Scootertaj
97
0
1. Let the joint pdf be f(x,y) = 2 ; 0<x<y<1 ; 0<y<1
Find E(Y|x) and E(X|y)

Homework Equations



E(Y|x) = [itex]\int Y*f(y|x)dy[/itex]
f(y|x) = f(x,y) / f(x)

The Attempt at a Solution


f(x) = [itex]\int 2dy[/itex] from 0 to y = 2y
f(y|x) = f(x,y)/f(x) = 1/2y
E(Y|x) = [itex]\int Y/2Y dy[/itex] from x to 1 = [itex]\int 1/2 dy[/itex] from x to 1
= -(x-1)/2
= (1-x)/2

The answer is supposed to be (1+x)/2
 
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  • #2
Your expression for f(x) is wrong. It should be a function of x, not of y. Try drawing a picture of the region where f(x,y) is nonzero. Then answer this question: for a fixed value of x, what values of y will give you a nonzero f(x,y)?
 
  • #3
The only other way I can think of doing f(x) would be to integrate from 0 to 1 instead. f(x) is defined as the integral of the joint pdf in terms of y.

So, we could get integral(2dy) from x to 1?
 
  • #4
Scootertaj said:
The only other way I can think of doing f(x) would be to integrate from 0 to 1 instead. f(x) is defined as the integral of the joint pdf in terms of y.

So, we could get integral(2dy) from x to 1?
Correct, from x to 1 (not as 0 to 1 as you wrote in the previous paragraph). Also be sure to state which values of x this is valid for.
 
  • #5
That will give us 2(1-x) so f(y|x) = 1/(1-x)

I'm confused how this will give (1+x)/2 for E(y|x)
 
  • #6
Just kidding I worked it out, thanks.
 
  • #7
Scootertaj said:
That will give us 2(1-x) so f(y|x) = 1/(1-x)

I'm confused how this will give (1+x)/2 for E(y|x)

That's the answer I got. What integral are you calculating for E(y|x)?

[edit] Cool, I see you got it.
 

What is Conditional Expectation?

Conditional expectation is a mathematical concept that represents the expected value of a random variable given certain conditions or information. It can be seen as a prediction or estimation of the value of a random variable based on other known variables or events.

How is Conditional Expectation calculated?

The calculation of conditional expectation depends on the type of probability distribution being used. In discrete cases, it is calculated by finding the weighted sum of the possible outcomes, where the weights are the conditional probabilities. In continuous cases, it is calculated by integrating the product of the random variable and its conditional probability density function.

What is the relationship between Conditional Expectation and Conditional Probability?

Conditional expectation and conditional probability are closely related concepts. Conditional expectation is the expected value of a random variable given certain conditions, while conditional probability is the probability of an event occurring given certain conditions. Conditional expectation can be seen as a function of conditional probability.

What are some applications of Conditional Expectation?

Conditional expectation has various applications in fields such as statistics, finance, and economics. It is used in predictive modeling, risk assessment, and decision making. It also plays a significant role in Bayesian statistics and machine learning algorithms.

What is the Law of Iterated Expectation?

The Law of Iterated Expectation states that the expected value of a random variable can be found by taking the expectation of its conditional expectation. In other words, if X and Y are random variables, the expected value of X can be calculated by taking the expected value of Y and then taking the expected value of X given Y.

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