Probability - Conditional Expectation

In summary, my professor explained this concept absolutely horribly and I have no idea how to do these problems.
  • #1
mathmajor23
29
0
My professor explained this concept absolutely horribly and I have no idea how to do these problems.

Let A and B be independent Poisson random variables with parameters α and β, respectively. Find the conditional expectation of A given A + B = c.
(Hint: For discrete random variables, there is no conditional density. Use the definition of conditional probability.)

Attempt:
Starting with the definition, f(A | A + B = c) = [f(A, A+B=c)] / [f(A+B=c)]

Not sure how to proceed.
 
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  • #2
We will have to work with A+B. Do you know the probability distribution of this??
 
  • #3
mathmajor23 said:
My professor explained this concept absolutely horribly and I have no idea how to do these problems.

Let A and B be independent Poisson random variables with parameters α and β, respectively. Find the conditional expectation of A given A + B = c.
(Hint: For discrete random variables, there is no conditional density. Use the definition of conditional probability.)

Attempt:
Starting with the definition, f(A | A + B = c) = [f(A, A+B=c)] / [f(A+B=c)]

Not sure how to proceed.

It is best to be clear and to use correct notation: you want P{A=k|A+B=c} for the possible values of k in {0,1,2,...}. So, you need to compute P{A=k & A+B=c} in the numerator (and, of course, you need P{A+B=c} in the denominator).

Do you know the distribution of A+B? It should be in your textbook or course notes; if not, look on-line, or work it out for yourself from first principles, using the distributions of A and B and the formula for the distribution of a sum of independent random variables (really: it is not that hard!).

RGV
 
Last edited:
  • #4
The distribution for a Poisson distribution is p(x) = [e^(-λ)*λ^x] / x!
 
  • #5
Yes, that is the distribution for A and B with [itex]\lambda=\alpha[/itex] and [itex]\lambda=\beta[/itex] respectively.

But we are asking about the distribution of A+B.
 
  • #6
A + B also has a Poisson distribution with parameters Poisson(A+B), as the sum of independent Poisson random variables has a Poisson distribution.
 
  • #7
OK, that's good. Now we want to figure out (for fixed c)

[tex]f(A=x~|~A+B=c)[/tex]

In order to find to, we want to find

[tex]f(A=x,~A+B=c)[/tex]

Of course, this is equal to

[tex]f(A=x,~B=c-x)[/tex]

Can you find this?? This is just a two-dimensional pmf. Remember that A and B are independent, so you can find it easily.

Then we also nee to find

[tex]f(A+B=c)[/tex]

This should be easy since you just figured out the distribution of A+B.
 
  • #8
Not sure how to go about finding f(A=x , B=c−x)
 
  • #9
mathmajor23 said:
Not sure how to go about finding f(A=x , B=c−x)

You told us that A and B are independent. What do you think that means?

RGV
 
  • #10
Can anyone show me this problem step by step? I'm not picking up on any of this question, which is why I posted this.
 
  • #11
mathmajor23 said:
Can anyone show me this problem step by step? I'm not picking up on any of this question, which is why I posted this.

No, we can't. That is not how this forum works. However, I will give you a hint: if your professor did not explain things to your satisfaction, and if, for some reason you do not have access to course notes or to a textbook, then *look online*. Google 'independent + probability' to turn up hundreds of articles at various levels of sophistication, from step-by-step explanations to abstract discussions.

RGV
 
Last edited:
  • #12
P(A|A+B=c)
= P(A|B=c-A)
= P(A and B=c-A) / P(B=c-A)

=P(α + β) / P(β) ?
 
  • #13
mathmajor23 said:
P(A|A+B=c)
= P(A|B=c-A)
= P(A and B=c-A) / P(B=c-A)

=P(α + β) / P(β) ?

I have no idea what you mean by P(α + β) or P(β). I know what α and β are, and I know what is meant by P(A=u) or P(B=v) and how to write them in terms of α, β, u and v, but I cannot figure out your P(α+β), etc. Anyway, I certainly would get something very different from what you wrote.

RGV
 

1. What is conditional expectation?

Conditional expectation is a mathematical concept that calculates the expected value of a random variable, given that another random variable or event has already occurred.

2. How is conditional expectation calculated?

The conditional expectation of a random variable X given a random variable Y is calculated using the formula E(X|Y) = ∑x(x|y)*P(x|y), where x represents the possible values of X, y represents the possible values of Y, and P(x|y) represents the conditional probability of X given Y.

3. What is the relationship between conditional expectation and conditional probability?

Conditional expectation and conditional probability are closely related, as they both involve using information about one event or random variable to make predictions about another event or random variable. However, conditional expectation is a measure of the expected value, while conditional probability is a measure of the likelihood of an event occurring.

4. How is conditional expectation used in real life?

Conditional expectation has various applications in fields such as finance, economics, and statistics. It is commonly used to make predictions and inform decision-making, such as in insurance risk assessment or stock market forecasting.

5. What is the difference between conditional expectation and unconditional expectation?

Unconditional expectation is the expected value of a random variable without any conditions or additional information. On the other hand, conditional expectation takes into account a specific event or random variable and uses that information to calculate the expected value of another random variable.

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