How do I derive this expression for conditional probability?

In summary, the equation provided in the problem statement cannot be correct because it is missing a factor of ##p(1-p)##, which is necessary to account for the independence of the Bernoulli random variables.
  • #1
Eclair_de_XII
1,083
91
Homework Statement
"Let ##Y_i## be independent Bernoulli r.v.'s. Let ##T=1## if ##Y_1=1## and ##Y_2=0## and ##T=0## otherwise. Let ##W=\sum Y_i##. Prove that ##P(T=1|W=w)=\frac{w(n-w)}{n(n-1)}##.
Relevant Equations
##P(A|B)=\frac{P(A\cap B)}{P(B)}##
An answer from another part in the problem: ##E(T)=p(1-p)##
##P(T=1|W=w)=\frac{P(\{T=1\}\cap\{W=w\})}{P(W=w)}=\frac{\binom {n-2} {w-1} p^{w-1}(1-p)^{(n-2)-(w-1)}}{\binom n w p^w (1-p)^{n-w}}=\frac{(n-2)!}{(w-1)!(n-w-1)!}\frac{w!(n-w)!}{n!}\frac{1}{p(1-p)}=\frac{w(n-w)}{n(n-1)}(p(1-p))^{-1}##.

I cannot seem to get the terms with ##p## out of my expression.
 
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  • #2
Eclair_de_XII said:
Problem Statement
"Let ##Y_i## be independent Bernoulli r.v.'s. Let ##T=1## if ##Y_1=1## and ##Y_2=0## and ##T=0## otherwise. Let ##W=\sum Y_i##. Prove that ##P(T=1|W=w)=\frac{w(n-w)}{n(n-1)}##.
Relevant Equations
##P(A|B)=\frac{P(A\cap B)}{P(B)}##
An answer from another part in the problem: ##E(T)=p(1-p)##

##P(T=1|W=w)=\frac{P(\{T=1\}\cap\{W=w\})}{P(W=w)}=\frac{\binom {n-2} {w-1} p^{w-1}(1-p)^{(n-2)-(w-1)}}{\binom n w p^w (1-p)^{n-w}}=\frac{(n-2)!}{(w-1)!(n-w-1)!}\frac{w!(n-w)!}{n!}\frac{1}{p(1-p)}=\frac{w(n-w)}{n(n-1)}(p(1-p))^{-1}##.

I cannot seem to get the terms with ##p## out of my expression.

Your second equality is wrong.

For ##w \geq 1##, we have:

##P(T=1, W = w) = P(Y_1 = 1, Y_2 = 0, \sum_{i=3}^n Y_i = w-1) = P(Y_1 = 1)P(Y_2 = 0)P(\sum_{i=3}^n Y_i = w-1)## (here the independence of ##(Y_n)_n## was crucial) and the first two factors give the factor ##p(1-p)## that you are missing.

For ##w=0##, it is easily checked that the equality you want to prove holds as well.
 
  • #3
Okay, thanks.
 

1. What is conditional probability?

Conditional probability is a measure of the likelihood of an event occurring given that another event has already occurred. It is denoted as P(A|B), which reads as "the probability of A given B."

2. How do I calculate conditional probability?

To calculate conditional probability, you need to know the probability of the two events occurring separately and the probability of both events occurring together. The formula for conditional probability is P(A|B) = P(A and B) / P(B).

3. What is the difference between conditional probability and joint probability?

Conditional probability is the likelihood of an event occurring given that another event has already occurred. Joint probability, on the other hand, is the likelihood of two events occurring together. Conditional probability is calculated using joint probability.

4. How do I derive the expression for conditional probability?

The expression for conditional probability can be derived using the definition of probability and the multiplication rule. It involves dividing the joint probability of two events by the probability of one of the events occurring.

5. Can conditional probability be applied to more than two events?

Yes, conditional probability can be applied to more than two events. In such cases, the formula becomes P(A|B and C) = P(A and B and C) / P(B and C).

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