Saturday, May 2, 2009

Baye's Theorem: Cut it out!

There is this big crowd of people who are desperately trying to to be a part of community that knows the answer to: "Why did that Bayesian traveler didn't crossed the road?"

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:

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?


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!
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

People with cancer AND positive mamography = (Any Guesses???)
Yes, its 1% * 0.10304%!


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!!! :)