Probability of having disease given positive test result I am trying to solve a puzzle and have struck in some fundamental concepts of probability.
In an epidemic, its found that only one in 10000 people actually suffer from the rare disease. So  the probability that I am positive will be

P1= 1/10000

But there is a medical test also which has 99% accuracy. Unfortunately my test result is positive. As per this test, the probability that I am positive will be

P2= 99/100

Now I have two probabilities P1 and P2.  The first question that comes to my mind is how can we have two probabilities for a single event (i.e. being "positive")? Why they are different?
Secondly, which one should I trust and why ?
Or what is the actual probability of me being found positive?
Edit
The specific question is if 100000 people are tested, and the test says I am positive, then what is the probability that I am actually suffering from the disease given the test results?

Both existing answers are absolutely right here, but I thought I might be able to make this a little more intuitive.
1. The base rate is the prior probability
If one in 10,000 people have the disease (this is called the base rate, or prevalence), the probability of any random person, about whom we have no other information, having the disease is $\frac{1}{10000} = .0001$. That's also the probability that you have it, before you take a test or check whether you have symptoms.
2. Accuracy is complicated
It doesn't make sense to talk about a test being accurate or not in general terms. Instead, you can talk about the sensitivity of the test (probability of being positive when taken by people who do in fact have the disease) and the specificity (probability of being negative when taken by people who do not have the disease). There's no reason to expect these probabilities to be the same. (See wikipedia for more).
3. Putting it all together
You want to know the positive predictive value: the probability of having the disease, given that your test is positive. You need to know the base rate/prevalence, sensitivity, and specificity to calculate this, and the equation is on Wikipedia:

In short (because the other answers cover this well), you want to figure out how how many people have the disease and get positive tests (true positives), and how many people don't have the disease but get positive tests (false positives), and use this to calculate what proportion of all the people with positive tests are true positives.
