Statistics with Baye's theorem and partitions

AI Thread Summary
The discussion revolves around solving statistical problems using Bayes' theorem and understanding distributions. Clarification is sought for Q1 regarding the distribution table when Y=2, with a suggestion to include (2,2) as a valid option. For Q3, the concept of independence among three events is explored, emphasizing that their joint probability equals the product of their individual probabilities. Concerns are raised about ensuring that the total probability sums to one, particularly in the context of conditional probabilities. The conversation highlights the need for precise calculations and understanding of probability distributions in these scenarios.
rock.freak667
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Well the questions and solutions are in one for some and I will type out the rest.

Q6
http://img249.imageshack.us/img249/2757/47026197.jpg

Q4
http://img237.imageshack.us/img237/1802/93774799.jpg
Q3
http://img3.imageshack.us/img3/5919/34981698.jpg

Q1
http://img19.imageshack.us/img19/7307/25729239.jpg

I need some help doing Q3 and I need some clarification with Q1.

With Q1, if Y=2, then there are only two possibilities, (1,2) and (1,2). So X can only be equal to 3 thus I am not sure how to write out the distribution table.http://img222.imageshack.us/img222/9981/95680883.jpg
 
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for Q1, there's no reason not to include (2,2) as well (twice)

for Q3, three events would be independent if p(A and B and C) = p(A)p(B)p(C), and the three possible pairs are independent (e.g A and C), just a natural extension from two events
 
Mulder said:
for Q1, there's no reason not to include (2,2) as well (twice)
Right, but wouldn't then my distribution not all add up to one either way? Σall x P(X=x)≠1

Mulder said:
for Q3, three events would be independent if p(A and B and C) = p(A)p(B)p(C), and the three possible pairs are independent (e.g A and C), just a natural extension from two events

This I know, but I don't know what numbers I'd write down for P(A) and so on.
 
rock.freak667 said:
Right, but wouldn't then my distribution not all add up to one either way? Σall x P(X=x)≠1


You should just have Σall x P(X=x | Y=2) =1



This I know, but I don't know what numbers I'd write down for P(A) and so on.


I drew the square with corners at 0, (0,1), (1,1), (1,0) ao P(A) = P(B) =1/2 etc.
 
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