Probability Conditional Expectation

In summary, X and Y are independent Poisson random variables with respective parameters λ and 2λ. To find E[Y − X|X + Y = 10], we need to first calculate P(X = k | X + Y = n) using the fact that (X|X+Y=n) is a familiar discrete random variable that ranges from 0 to n. Then, we can use this to find the distribution of (X_a|X_a+X_b=n) for independent Poisson random variables X_a and X_b.
  • #1
ctownballer03
8
0
Suppose X and Y are independent Poisson random variables with respective parameters λ and 2λ.
Find E[Y − X|X + Y = 10]3: I had my Applied Probability Midterm today and this question was on it. The class is only 14 people and no one I talked to did it correctly. The prof sent out an e-mail saying how no one did it correctly and we need to work on it and get it figured out and corrected by our next class and frankly I'm still super stuck (my professor is pretty useless, I can't utilize him as resource for anything in this class). Anyways, this is my attempt at doing this however I realize that I've made a mistake and even though X and Y are independent, Y=y and X+Y=10 are NOT independent events so the cancellation that I did is not a legal move.. Any other ideas of how to approach this problem, I feel like I'm back to square one and I'm not sure where to go. Thank you!

ElQtujT.jpg
 
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  • #2
ctownballer03 said:
Suppose X and Y are independent Poisson random variables with respective parameters λ and 2λ.
Find E[Y − X|X + Y = 10]3: I had my Applied Probability Midterm today and this question was on it. The class is only 14 people and no one I talked to did it correctly. The prof sent out an e-mail saying how no one did it correctly and we need to work on it and get it figured out and corrected by our next class and frankly I'm still super stuck (my professor is pretty useless, I can't utilize him as resource for anything in this class). Anyways, this is my attempt at doing this however I realize that I've made a mistake and even though X and Y are independent, Y=y and X+Y=10 are NOT independent events so the cancellation that I did is not a legal move.. Any other ideas of how to approach this problem, I feel like I'm back to square one and I'm not sure where to go. Thank you!

ElQtujT.jpg

If ##X \sim \text{Po}(a)## and ##Y \sim \text{Po}(b)##, what is ##P(X = k | X + Y = n)##?

Hint: ## (X|X+Y=n)## is a familiar discrete random variable, whose values range from ##0## to ##n##.
 
  • #3
Ray Vickson said:
If ##X \sim \text{Po}(a)## and ##Y \sim \text{Po}(b)##, what is ##P(X = k | X + Y = n)##?

Hint: ## (X|X+Y=n)## is a familiar discrete random variable, whose values range from ##0## to ##n##.

Things have just got interesting. I may have to post again later tonight if I get stuck but you have put me on the right track. So the PMF of X=k|X+Y=n where X+Y are poisson RVs is going to be a binomial.
 
  • #4
Solved it now.

Thank you for the hint.
 
  • #5
ctownballer03 said:
Solved it now.

Thank you for the hint.

Just as a matter of interest: for independent ##X_a \sim \text{Po}(a)## and ##X_b \sim \text{Po}(b)##, what did you obtain as the distribution of ##(X_a|X_a+X_b=n)##?
 

What is "probability conditional expectation"?

Probability conditional expectation is a concept in statistics that refers to the expected value of a random variable given the knowledge of another random variable. It is denoted by E(Y|X) and represents the average value of Y when X is known.

How is "probability conditional expectation" calculated?

The formula for calculating probability conditional expectation is: E(Y|X) = ∑ y * P(Y=y | X), where y is the possible values of Y and P(Y=y | X) is the conditional probability of Y given X.

What is the difference between "probability conditional expectation" and "ordinary expectation"?

The main difference between the two is that ordinary expectation calculates the average value of a random variable without any additional knowledge, while probability conditional expectation takes into account the knowledge of another random variable. In other words, ordinary expectation is a single-value measure, while probability conditional expectation is a function of another random variable.

Why is "probability conditional expectation" important in statistics?

Probability conditional expectation is important in statistics because it allows us to make predictions and draw conclusions based on the knowledge of another variable. It is also a useful tool in analyzing and understanding complex data sets and can help in making informed decisions.

What are some real-world applications of "probability conditional expectation"?

Probability conditional expectation has various applications in fields such as finance, economics, and insurance. For example, it can be used to calculate the expected return on investments, predict the probability of default in loan payments, and estimate the cost of insurance premiums based on certain risk factors. It is also used in weather forecasting, medical diagnosis, and other fields where the outcome of one event is dependent on another.

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