Second life traffic not updating

Bayesian reasoning is apparently one of those things which, like quantum mechanics or the Wason Selection Test, is inherently difficult for humans to grasp with our built-in mental faculties. Here you will find an attempt to offer an explanation of Bayesian reasoning - an excruciatingly gentle introduction that invokes all the human ways of grasping numbers, from natural frequencies to spatial visualization.The intent is to convey, not abstract rules for manipulating numbers, but what the numbers mean, and why the rules are what they are (and cannot possibly be anything else).Expressed as a proportion, this is 80/1,030 or 0.07767 or 7.8%.To put it another way, before the mammography screening, the 10,000 women can be divided into two groups: As you can check, the sum of all four groups is still 10,000.Here's an alternate version of the problem on which doctors fare somewhat better: 10 out of 1000 women at age forty who participate in routine screening have breast cancer.

The proportion of the cancer patients (A B) within the complete set of patients (A B C D) is the same as the 1% prior chance that a woman has cancer: (80 20) / (80 ) = 100 / 10000 = 1%.To see that the final answer always depends on the original fraction of women with breast cancer, consider an alternate universe in which only one woman out of a million has breast cancer.Even if mammography in this world detects breast cancer in 8 out of 10 cases, while returning a false positive on a woman without breast cancer in only 1 out of 10 cases, there will still be a hundred thousand false positives for every real case of cancer detected.The correct answer is 7.8%, obtained as follows: Out of 10,000 women, 100 have breast cancer; 80 of those 100 have positive mammographies.From the same 10,000 women, 9,900 will not have breast cancer and of those 9,900 women, 950 will also get positive mammographies.

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