AI - week 2 Bayes Networks, probably the best networks in the world

What are the chances of that?

After an easy introduction, via tree searching, last week, we're into the the thick of it with Bayes Networks and stochastic reasoning.

The good reverend Bayes

Bayes networks deal with uncertainty, they can answer questions such as -given that my cancer test was positive -what are the chances that I have cancer?  I'm not going to go into the details here, there are several resources on the web -but the answer is of the form

P(C|T) = ( P(T|C) . P(C) ) / P(T)

and likely to be lower than you think. P(C|T) is the chances of you having cancer given the test, P(T|C) is the chance of the test being positive if you have cancer -which I guess would come from the testing of the test, P(C) is the chance of you having cancer in general -which would come from actuarial tables or the like and P(T) is the chance of the test being positive whether o not you have cancer.

The course goes into more depth than this, showing how to reason from one test result to another, say, and how to chain probabilities.

All good stuff -but my brain aches.