How to Solve a Probability Problem with Independent Stochastic Variables?

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Homework Help Overview

The discussion revolves around a probability problem involving two independent stochastic variables, X and Y, both of which are Poisson distributed. The original poster seeks to demonstrate a specific conditional probability involving these variables given their distributions and a parameterization involving λ and μ.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the formulation of the conditional probability P(X=l|X+Y=m) and explore the relationship between the variables through the use of probability distribution properties. There are attempts to clarify the correct expressions for joint probabilities and conditional probabilities.

Discussion Status

There is an ongoing exploration of the problem, with participants providing insights and corrections to each other's statements. Some participants have pointed out potential errors in reasoning and have suggested re-evaluating the expressions used. The discussion is active, with various interpretations being considered.

Contextual Notes

Participants are working under the constraints of a homework assignment, which may impose specific requirements on the approach and presentation of the solution. There is a noted confusion regarding the correct application of the binomial formula and the parameters involved in the problem.

Hummingbird25
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Hi People,

I have this Probability Problem which is given me a headach,

Given two independent Stochastic variables (X, Y) where X is Poisson distributed [tex]Po(\lambda)[/tex] and Y is Poisson distributed [tex]Po(\mu)[/tex].

Where [tex]\lambda[/tex], [tex]\mu > 0[/tex]. Let [tex]m \geq 0[/tex] and [tex]p = \frac{\lambda}{\lambda+ \mu}[/tex]

By the above I need to show, that

[tex]P(X=l| X+Y=m) = \left( \begin{array}{cc} l \\ m \end{array} \right ) p^l (1-p)^{(l-m)}[/tex]

Proof:

Its know that

[tex]\left( \begin{array}{cc} l \\ m \end{array} \right ) = \frac{l!}{m!(1-m)!}[/tex]

Wherefore the Binormal formula can be written as

[tex]\frac{l!}{(m!(l-m))!} p^l (1-p)^{(l-m)}[/tex]

for [tex]m \geq 0[/tex] I get:[tex]\frac{-(\frac{\lambda + \mu}{\mu})^{l} \cdot p^{l} \cdot \mu}{l!(l!-1) \cdot (\lambda + \mu)}[/tex]

Any surgestions on how to processed from here?

Sincerely Yours
Hummingbird25
 
Last edited:
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No, no suggestions on how to proceed from there since you seem to be going at it backwards.

For any probability distribution, P(A and B)= P(A|B)*P(B) so that
P(A|B)= P(A and B)/P(B).

You are looking for P(X= l| X+ Y= m) so A is "X= l" and B is "X+ Y= m".
Then A and B is "X= l and X+ Y= m" which is the same as "X= l and Y= m-l". Now P(A and B)= P(X= l)*P(Y= m) .
 
Hi Hall,

Hello since X and Y is Poisson distributed then

[tex]P(x=l) * P(Y=m) = \frac{\lambda^l}{l !} e^{-\lambda} * \frac{\lambda^m}{m !} e^{-\mu}[/tex]

Is this the next?

Sincerley Yours
Hummingbird25
 
Last edited:
I think HallsofIvy made a small mistake in his last statement. He probably meant "P(A and B)= P(X= l)*P(Y= m-l)".
 
Last edited:
Hello since X and Y is Poisson distributed then

[tex]P(X=l) * P(Y=m-l) = \frac{\lambda^l}{l !} e^{-\lambda} *[/tex] (what do I need to add here then)?

Is this the next?

Sincerley Yours
Hummingbird25

pizzasky said:
I think HallsofIvy made a small mistake in his last statement. He probably meant "P(A and B)= P(X= l)*P(Y= m-l)".
 
[tex]P(X=l) * P(Y=m-l) = \frac{\lambda^l}{l !} e^{-\lambda} * \frac{\mu^{m-l}}{(m-l)!} e^{-\mu}[/tex]

And Hummingbird25, can I request that you check the question again? I tried solving the question and my answer was [tex]P(X=l| X+Y=m) = \left( \begin{array}{cc} m \\ l \end{array} \right ) p^l (1-p)^{(m-l)}[/tex]. Perhaps there is something wrong with my working...
 
Last edited:
Hello Pizzasky and thank You,

I looked at my assigment again I and discovered, that You result was the right one, I had (by mistake swapped l and m.

The right answer as You formulated:

[tex]\left( \begin{array}{cc} m \\ l \end{array} \right ) p^l (1-p)^{(m-l)}[/tex]Sincerely Hummingbird25

pizzasky said:
[tex]P(X=l) * P(Y=m-l) = \frac{\lambda^l}{l !} e^{-\lambda} * \frac{\mu^{m-l}}{(m-l)!} e^{-\mu}[/tex]

And Hummingbird25, can I request that you check the question again? I tried solving the question and my answer was [tex]P(X=l| X+Y=m) = \left( \begin{array}{cc} m \\ l \end{array} \right ) p^l (1-p)^{(m-l)}[/tex]. Perhaps there is something wrong with my working...
 
One last thing Pizzasky,

Which formula did You use to achive the binormal formula?

Sincerely Yours
Hummingbird25

pizzasky said:
[tex]P(X=l) * P(Y=m-l) = \frac{\lambda^l}{l !} e^{-\lambda} * \frac{\mu^{m-l}}{(m-l)!} e^{-\mu}[/tex]

And Hummingbird25, can I request that you check the question again? I tried solving the question and my answer was [tex]P(X=l| X+Y=m) = \left( \begin{array}{cc} m \\ l \end{array} \right ) p^l (1-p)^{(m-l)}[/tex]. Perhaps there is something wrong with my working...
 
Reply

You can use the substitution [tex]p = \frac{\lambda}{\lambda+ \mu}[/tex] to get the Binomial formula. Also, try to figure out what expression "1-p" corresponds to.
 

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