To them it looks like the world is divide into two parts: Smart-ass people who knows what Bayes is all about; and they themselves!

To all my friends on the other side, here's a chance to grasp Bayes in most intuitive way (I assume you know basic probability stuff)!

The Problem:

(Courtesy: http://yudkowsky.net/rational/bayes)

1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

Variables:

Lets assign the good old variables to the events we have in the problem:

A: People with cancer

B: People with positive mamographies

Just to make the conventions clear:

P(A): Probability that a person has cancer

P(B): Probability that person will get a positive mamography

Note: P(A|B) in general means probability of A, given that B happens!

So,

P(A|B): Probability that a person has cancer, given he got a positive mamography

P(B|A): Probability that person gets a positive mamography, given he has cancer (i feel sad for him though)

That's a lot of dumb variables!!!

Lets move to interesting things now...

What we know from the problem:

P(A) = 1%

P(B) = ??

P(B|A) = 80%

P(A|B) = ?? (This the real QUESTION)

Check-Point: If you are clear with everything till now, i guarantee you will understand Bayes theorem in just some minutes from now!!!

Breaking it down:

Finding P(B) (persons with positive mamographies is not difficult).

P(B) = Cancerians with positive mamographies + Non-Cancerians with positive mamographies

So, P(B) = 1%*80% + 9.6%*(1-1%) = 0.10304

Bayes in Action:

A person got positive mamography and we need to find the probability he actually has cancer. This would be simple:

Required Probability = People with cancer and positive mamography/People with positive mamography

Denominator is P(B). We already know that!

On to the numerator:

P(A)% (1%) people have cancer

Out of these P(B|A)% (0.10304*) people have positive mamographies

So,

People with cancer AND positive mamography = (Any Guesses???)

Yes, its 1% * 0.10304%!

=P(A)*P(B|A)

SO.

P(A|B) = P(A)*P(B|A)/P(B)

As simple as that!

That's what Bayes was trying to tell you...

I bet you did'nt knew it was this simple!!! :)

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