Is this conditional probability has derived correctly?

Click For Summary

Discussion Overview

The discussion centers on the derivation of a conditional probability formula involving a series of events \( A_i \) for \( i=1,2,...,n \). Participants explore the use of inclusion-exclusion principles in probability, questioning the notation and assumptions made in the derivation process.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents a conditional probability expression and seeks validation of its correctness.
  • Another participant challenges the notation used, suggesting that it may not accurately represent the intended summation of probabilities.
  • A clarification is made regarding the interpretation of terms involving combinations, emphasizing the need for consistent indexing across events.
  • There is a discussion about the inclusion-exclusion property of probability and its application to the problem at hand.
  • Some participants express uncertainty about the implications of stating that the events \( A_i \) are "of the same type" and how this affects the derivation.
  • One participant notes that the derivation relies on a series of equalities that have not been explicitly stated, which could affect the validity of the arguments presented.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the correctness of the derivation. There are multiple competing views regarding the notation and assumptions involved, and the discussion remains unresolved.

Contextual Notes

There are limitations in the clarity of the assumptions regarding the events \( A_i \) and their relationships, particularly concerning independence and the implications of the events being "of the same type." The notation used may also lead to misunderstandings about the intended mathematical expressions.

sabbagh80
Messages
38
Reaction score
0
Hi friends,

The problem:
Assume the events [itex]A_i[/itex] for [itex]i=1,2,...,n[/itex] are of the same type. please trace the following relations.

[itex]P(X│A_1 A_2…A_n )= \frac{P(X)}{P(A_1 A_2…A_n )} P(A_1 A_2…A_n│X)=[/itex]

[itex]\frac{P(X)}{P(A_1 A_2…A_n )}[ (-1)^{n-1} P(⋃_{i=1}^{n} A_i │X)+(-1)^{n-2} {n \choose 1} P(A_1│X)+[/itex]
[itex](-1)^{n-3} {n \choose 2}P(A_1 A_2│X)+…+{n \choose n-1}P(A_1 A_2…A_{n-1}│X) ][/itex]

[itex]= \frac{1}{P(A_1 A_2…A_n )} [ (-1)^{n-1} P(X|⋃_{i=1}^{n}A_i ) P(⋃_{i=1}^{n}A_i)+ (-1)^{n-2} {n \choose 1}P(X│A_1 )P(A_1 )+(-1)^{n-3} {n \choose 2} P(X│A_1 A_2 )P(A_1 A_2 )+[/itex]
[itex]…+{n \choose n-1}P(X│A_1 A_2…A_{n-1} )P(A_1 A_2…A_{n-1}) ][/itex]

[itex]=(-1)^{n-1} \frac{P(⋃_{i=1}^{n}A_i )}{P(A_1 A_2…A_n )} P(X|⋃_{i=1}^{n}A_i )<br /> +(-1)^{n-2} \frac{P(A_1 )}{P(A_1 A_2…A_n )} {n \choose 1} P(X│A_1 )+(-1)^{n-3} \frac{P(A_1 A_2 )}{P(A_1 A_2…A_n )} {n \choose 2} P(X│A_1 A_2 )+[/itex] [itex]…+{n \choose n-1} \frac{P(A_1 A_2…A_{n-1})}{(P(A_1 A_2…A_n)} P(X│A_1 A_2…A_{n-1} )[/itex]

Is everything OK? thanks a lot in advance for your involvement!
 
Last edited:
Physics news on Phys.org
sabbagh80,

That doesn't make sense. It's probably because you intend notation like [itex]{n \choose 2}P(A_1 A_2│X)[/itex] to mean [tex]\sum_{\{(i,j):1 \leq i \le n, 1 \leq j \leq n, i \neq j\}} p(A_i A_j|X)[/tex].

And I don't understand why the term [itex]P(\cup_{i=1}^n A_i|X)[/itex] would need a factor of [itex](-1)^{n-1}[/itex] with it.
 
Last edited:
Stephen Tashi,
By [itex]{n \choose 2}P(A_1 A_2│X)[/itex], I don't mean:
[itex]\sum_{\{(i,j):1 \leq i \le n, 1 \leq j \leq n, i \neq j\}} p(A_i|X)p(A_j|X)[/itex]
I mean [itex]\sum_{\{(i,j):1 \leq i \le n, 1 \leq j \leq n, i \neq j\}} p(A_iA_j|X)[/itex]

I just used the inclusion exclusion property of probability.
I want to write [itex]P(A_1 A_2…A_n│X)[/itex] according to the above property.
 
I was editing my message as you replied. See my revised version.

The point is that the use of the specific indices 1 and 2 in your expression does not convey the fact that you want the indices to vary over all possible pairs of distinct indices. (You do want that, right? Otherwise the [itex]n \choose 2[/itex] doesn't make sense.)
 
sabbagh80 said:
I just used the inclusion exclusion property of probability.
I want to write [itex]P(A_1 A_2…A_n│X)[/itex] according to the above property.

That is the correct idea. Just get the notation fixed.
 
As I mentioned in my problem, I have:

[itex]\sum_{\{(i,j):1 \leq i \le n, 1 \leq j \leq n, i \neq j\}} p(A_iA_j|X)={n \choose 2}P(A_1 A_2│X)[/itex]
 
So, you mean that it is OK? Am I right?
thanks
 
Last edited:
sabbagh80 said:
So, you mean that it is OK? Am I right?
thanks

It depends on what you mean when you say that the [itex]A_i[/itex] are events "of the same type".

Must go to an appoinment now. Back later.
 
Your idea is basically to take the formula
[tex]P( \cup_{i=1}^n A_i) = \sum_{i=1}^n P(A_i) - ...[/tex]
and solve for the last term on the right hand side, which is either plus or minus [itex]P(\cap_{i=1}^n A_i)[/itex].

The formula also applies when you restrict each of the [itex]A_i[/itex] by saying "given X", so that's OK.

What you aren't making clear (I think) is that you are assuming a large set of equalities such as:

[tex]P(A_1 A_2 | X) = P(A_3 A_4)| X) = P(A_3 A_5| X)[/tex] etc.
[tex]P(A_1 A_2 A_3| X) = P(A_2 A_3 A_4| X) = P(A_3 A_4 A_5 | X)[/tex] etc.
[tex]P(A_1 A_2 A_3 A_4| X) = P(A_2 A_3 A_4 A_5| X)[/tex] etc.


You should state this explicitly. You haven't revealed the application you are working on. It may be that you are dealing with independent events. Just remember that the fact that [itex]A1[/itex] and [itex]A_2[/itex] are independent does not mean that [itex]A_1|X[/itex] and [itex]A_2|X[/itex] must be independent.
 

Similar threads

Replies
2
Views
2K
  • · Replies 18 ·
Replies
18
Views
3K
  • · Replies 2 ·
Replies
2
Views
3K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 22 ·
Replies
22
Views
2K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 3 ·
Replies
3
Views
4K
  • · Replies 12 ·
Replies
12
Views
2K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K