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This question relates to Bayes law. I think my problem is im not sure of the name of the thing im trying to derive...

I have 2 variables a and b.

a = 1 or 0, b = 0...n

I have the data to calculate;

p(a = 1 and b) p(b)

for any b. Hence I can find p(a=1|b) = p(a = 1 and b)/p(b)

What I want is p(a=1|b), but 'given' that a = 1. I dont want this to be affected by p(b), hence im not trying to find p(b|a=1).

To explain further what i mean; If event a = 1, what is the prob it will happen at a certain b, independant of the frequency of occurences of different b's.

So I normalise;

[tex]\sum_{b = 0}^n p(a=1|b).N = 1[/tex]

Where N is a constant.

[tex] N = \frac{1}{\sum_{b = 0}^n p(a=1|b) }[/tex]

[tex] p(a=1,b) = \frac{p(a=1|b)}{\sum_{b = 0}^n p(a=1|b)}[/tex]

is that alright and does it have a name???

Many thanks in advance for any advice...

Hugh

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# Bayes law

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