A classic example of Bayes' theorem in action comes from the world of disease testing. This is a really good example because it shows the importance of the prior distribution. So here's the problem: imagine that you're concerned that you have COVID-i 9, something that currently affects roughly 7 in 1000 people in the US, and you go down to the county health services to take a COVID test. The doctor tells you that the sensitivity of the test is 70%. which is to say that if you have the disease the test will read positive 70% of the time. We also know that the test has a specicity of 98%, which implies that 98% of the time, if you don't have the disease, the test will read negative. Unfortunately, your test comes back positive. Compute the probability that you actually have the disease. Using python (matplotllb). generate a plot showing the probability above as a function ofthe specicity (the false positive rate). Devise a simple May for reducing the false positive rate