Conditional probability. Unable to prove the general result.

In summary: That's all I had to say.In summary, Rohatgi's book, "An introduction to Probability and Statistics," provides the solution to a problem in which the probability of selecting a red marble at any draw is r/(r+g). After struggling with the summations and products in his first attempt, the Hungarian mathematician found an expression for P(n) that can be calculated using the law of total expectation and the index of summation 1/(r+g+n-1). Thanks for your help!
  • #1
Karlx
75
0
Hi everybody.
I keep on reading Rohatgi's book "An introduction to Probability and Statistics" and I have worked out the following problem:

"An urn contains r red marbles and g green marbles. A marble is drawn at random and its color noted. Then the marble drawn, together with c > 0 marbles of the same color, are returned to the urn. Suppose that n such draws are made from the urn. Prove that the probability of selecting a red marble at any draw is r/(r+g)."

I have obtained the following expression for the required probability:

[tex]

P(n) = \frac{1}{\prod_{j=0}^{n-1}(r+g+jc)} \sum_{k=0}^{n-1}(\stackrel{n-1}{k}) \prod_{j=0}^{k}(r+jc) \prod_{j=0}^{n-k-2}(g+jc)

[/tex]

This expression gives the result r/(r+g) for values n=2,3.
I have been trying to prove it for all values of n>=2, by induction, but with no success.
Perhaps anyone of you could help me.
Thanks and Happy Halloween !
 
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  • #2
Hint: P(n) is the expected value of R(n)/(R(n)+G(n)) where R and G are the number of red and green balls after n draws.
 
  • #3
Thanks bpet.
Calculating P(n) as the expected value of R(n)/(r+g+(n-1)c) I arrive to the same expression I wrote in my first post.

So it remains the problem of proving that

P(n)=r/(r+g) (1)

for all values of n>0.

Finally, I achieved it.
I have proved that expression (1) holds for n=1,2,3.
Then, from the general expression of P(n) I have proved that P(n+1)=P(n).
It's not difficult, but in my first attempt I had some trouble with the indexes of summations and products.

Once again, thanks for your hint.
It has been useful for me in order to look the problem from another point of view.

By the way, I think this problem is known as the Pólya's urn.
The Hungarian mathematician gave it as an example of a martingale, that is, a random variable whose expected value at any time t is equal to his expected
value at a previous time s < t.
 
Last edited:
  • #4
That's interesting. Also the following identity might be useful:

E[R(n+1)|R(n),G(n)] = R(n)+c*R(n)/(R(n)+G(n))
 
  • #5
Once again, thanks a lot, bpet.
Your last expression has been, indeed, very useful !
In fact, after "fighting" with summations and products in my first approach to the problem, your expression is the clue to solve it without such a mess.
The new approach follows these steps:

1) Let R(n) and G(n) be respectively the number of red and green marbles at the n-th draw.

2) At the (n+1)th draw,
a) The probability that R(n+1)=R(n) is G(n)/(R(n)+G(n))
b) The probability that R(n+1)=R(n)+c is R(n)/(R(n)+G(n)

So, E[R(n+1)|R(n),G(n)] = R(n)+c*R(n)/(R(n)+G(n)), as you said.

3) And from this last expression,

P(n+1) = E[R(n+1)|R(n),G(n)]/(R(n+1)+G(n+1))
= [R(n)+c*R(n)/(R(n)+G(n))]/(r+g+nc)
= R(n)/(R(n)+G(n)) = P(n)

4) Thus P(n) = r/(r+g) for all n>0.

Thanks for your help !
 
  • #6
Almost there. The statements in step 3 can be made more precise with the law of total expectation E[X] = E[E[X|Y]].
 
  • #7
Yes, you are right.

P(n+1) = E[R(n+1)/(R(n+1)+G(n+1))]
=E{E[R(n+1)|R(n),G(n)]}/(R(n+1)+G(n+1))
=E{R(n)+c*R(n)/(R(n)+G(n))}/(r+g+nc)
=E{R(n)/(R(n)+G(n))}= P(n)

Thanks.
 

Related to Conditional probability. Unable to prove the general result.

What is conditional probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. It is represented as P(A|B), where A is the event of interest and B is the event that has already occurred.

How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of both events (P(A and B)) by the probability of the condition event (P(B)). This can be represented by the formula P(A|B) = P(A and B)/ P(B).

What is the difference between conditional probability and regular probability?

The main difference between conditional probability and regular probability is that conditional probability takes into account the occurrence of a specific event, while regular probability looks at the overall likelihood of an event occurring without any additional information or conditions.

Why is it sometimes difficult to prove the general result for conditional probability?

It can be difficult to prove the general result for conditional probability because it is often specific to the particular events and conditions being considered. In some cases, the general result may not exist or may be too complex to prove.

How is conditional probability used in real-world applications?

Conditional probability is used in many real-world applications, such as risk assessment, medical diagnosis, and weather forecasting. It allows us to make more accurate predictions and decisions by considering the likelihood of specific events happening in relation to other events that have already occurred.

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