How to Solve a Probability Problem with Independent Stochastic Variables?

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In summary, we have a problem involving two independent Stochastic variables (X, Y) where X is Poisson distributed Po(\lambda) and Y is Poisson distributed Po(\mu). We need to show that P(X=l| X+Y=m) = \left( \begin{array}{cc} m \\ l \end{array} \right ) p^l (1-p)^{(m-l)}. To do this, we use the formula P(A|B)= P(A and B)/P(B) and substitute in the values for A and B. After making some calculations, we get the formula P(X=l) * P(Y=m-l) = \frac{\lambda^l}{l !} e^{-\lambda
  • #1
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
 
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  • #2
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) .
 
  • #3
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
 
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  • #4
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)".
 
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  • #5
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)".
 
  • #6
[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...
 
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  • #7
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...
 
  • #8
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...
 
  • #9
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.
 

FAQ: How to Solve a Probability Problem with Independent Stochastic Variables?

What is probability?

Probability is a measure of the likelihood that an event will occur. It is typically expressed as a number between 0 and 1, where 0 indicates impossibility and 1 indicates certainty.

How is probability calculated?

Probability is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This is known as the probability formula: P(A) = number of favorable outcomes / total number of possible outcomes.

What is the difference between theoretical and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. Experimental probability is based on actual data and observations from an experiment or real-world situation.

What is the difference between dependent and independent events?

Dependent events are influenced by previous events, while independent events are not. In other words, the outcome of a dependent event is affected by the outcome of a previous event, while the outcome of an independent event is not affected by any previous events.

How is probability used in real life?

Probability is used in many real-life situations, such as weather forecasting, stock market analysis, and risk assessment in insurance and finance. It also plays a crucial role in decision-making, as it helps us estimate the likelihood of different outcomes and make informed choices.

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