  In this guise Bayes' theorem is particularly useful for inferring causes from their effects since it is often fairly easy to discern the probability of an effect given the presence or absence of a putative cause. For instance, physicians often screen for diseases of known prevalence using diagnostic tests of recognized sensitivity and specificity.  The sensitivity of a test, its "true positive" rate, is the fraction of times that patients with the disease test positive for it. The test's specificity, its "true negative" rate, is the proportion of healthy patients who test negative. If we let H be the event of a given patient having the disease, and E be the event of her testing positive for it, then the test's sensitivity and specificity are given by the likelihoods PH(E) and P~H(~E), respectively, and the "baseline" prevalence of the disease in the population is P(H).  Given these inputs about the effects of the disease on the outcome of the test, one can use (1.3) to determine the probability of disease given a positive test.  For a more detailed illustration of this process, see   Example 1 in the  Supplementary Document "Examples, Tables, and Proof Sketches".