I'm a CS major doing some work with image recognition in which I use Bayesian probability. I have to give a presentation on my work, and while I have no problem describing the CS portion, I'm less sure about the math being done. I'm having issues trying to explain Bayesian probability as a whole (I won't need to present on the specific math being done, just Bayesian probability in general). The problem seems to be that since I don't fully understand Bayesian probability, it's hard for me to accurately describe it.
I'm in need of a thorough, yet concise, description of Bayesian probability. Any suggestions or descriptions would be greatly appreciated!
EDIT
Here's an example that I've come across (details may change) in almost everything I've read about Bayesian probability.
10% of people have a disease and the test to detect it is 92% accurate, and has a 5% false-positive rate. If you tested positive, what is the probability that you have the disease?
The probability that you have it is $\frac{\text{chance of testing positive and having it}}{\text{the chance of having it } + \text{ chance of there being a false positive}}$
Which, for this example, would be $\frac{(.1*.92)}{(.1*.92) + (.9*.05)} = .67$
I don't understand why this is considered Bayesian probability and not just traditional probability.