This link has been bookmarked by 226 people . It was first bookmarked on 02 Mar 2006, by Hugh Bristic.
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kevinoempty
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scmoralSo you came here. Maybe you don't understand what the equation says. Maybe you understand it in theory, but every time you try to apply it in practice you get mixed up trying to remember the difference between p(a|x) and p(x|a), and whether p(a)*p(x|a)
statistics bayesian classification datamining ir textmining textanalysis linguistics bayes math mathematics algorithms categorization
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These two extreme examples help demonstrate that the mammography result doesn't replace your old information about the patient's chance of having cancer; the mammography slides the estimated probability in the direction of the result. A positive result slides the original probability upward; a negative result slides the probability downward.
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Most people encountering problems of this type for the first time carry out the mental operation of replacing the original 1% probability with the 80% probability that a woman with cancer gets a positive mammography. It may seem like a good idea, but it just doesn't work.
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The chance that a patient with a "positive" result has breast cancer is then the proportion of group A within the combined group A + C, or P*M / [P*M + (1 - P)*M], which, cancelling the common factor M from the numerator and denominator, is P / [P + (1 - P)] or P / 1 or just P.
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Which is common sense. Take, for example, the "test" of flipping a coin; if the coin comes up heads, does it tell you anything about whether a patient has breast cancer? No; the coin has a 50% chance of coming up heads if the patient has breast cancer, and also a 50% chance of coming up heads if the patient does not have breast cancer. Therefore there is no reason to call either heads or tails a "positive" result. It's not the probability being "50/50" that makes the coin a bad test; it's that the two probabilities, for "cancer patient turns up heads" and "healthy patient turns up heads", are the same.
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prior probability
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conditional probabilities
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the priors
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revised probability or the posterior probability
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if the two conditional probabilities are equal, the posterior probability equals the prior probability
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One thing that's confusing about this notation is that the order of implication is read right-to-left, as in Hebrew or Arabic
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Reading from left to right, "|" means "given"; reading from right to left, "|" means "implies" or "leads to".
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Looking at this applet, it's easier to see why the final answer depends on all three probabilities; it's the differential pressure between the two conditional probabilities, p(blue|pearl) and p(blue|~pearl), that slides the prior probability p(pearl) to the posterior probability p(pearl|blue).
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Even when the prior probability changes, the differential pressure of the two conditional probabilities always slides the probability in the same direction. If you learn the egg is painted blue, the probability the egg contains a pearl always goes up - but it goes up from the prior probability, so you need to know the prior probability in order to calculate the final answer.
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A study by Gigerenzer and Hoffrage in 1995 showed that some ways of phrasing story problems are much more evocative of correct Bayesian reasoning. The least evocative phrasing used probabilities. A slightly more evocative phrasing used frequencies instead of probabilities
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The most effective presentation found so far is what's known as natural frequencies
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the information about the prior probability is included in presenting the conditional probabilities
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In this case, you might as well just say that 30% of eggs are painted blue, since the probability of an egg being painted blue is independent of whether the egg contains a pearl.
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If the bottom bar were renormalized to the same length as the top bar, it would look like the left sector had expanded. This is why the proportion of "women with breast cancer" in the group "women with positive mammographies" is higher than the proportion of "women with breast cancer" in the general population - although the proportion is still not very high.
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The evidence of the positive mammography slides the prior probability of 1% to the posterior probability of 7.8%.
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You might intuit that since the test could have returned positive for health, but didn't, then the failure of the test to return positive must mean that the woman has a higher chance of having breast cancer
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Law of Conservation of Probability - not a standard term, but the conservation rule is exact. If you take the revised probability of breast cancer after a positive result, times the probability of a positive result, and add that to the revised probability of breast cancer after a negative result, times the probability of a negative result, then you must always arrive at the prior probability.
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p(A&B) is the same as p(B&A), but p(A|B) is not the same thing as p(B|A)
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For example, the two quantities p(cancer) and p(~cancer) have 1 degree of freedom between them, because of the general law p(A) + p(~A) = 1.
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p(positive|cancer) and p(~positive|cancer) also have only one degree of freedom between them; either a woman with breast cancer gets a positive mammography or she doesn't. On the other hand, p(positive|cancer) and p(positive|~cancer) have two degrees of freedom. You can have a mammography test that returns positive for 80% of cancerous patients and 9.6% of healthy patients, or that returns positive for 70% of cancerous patients and 2% of healthy patients, or even a health test that returns "positive" for 30% of cancerous patients and 92% of healthy patients.
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p(positive&cancer) = p(positive|cancer) * p(cancer)
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You should recognize this operation from the graph; it's the projection of the top bar into the bottom bar. p(cancer) is the left sector of the top bar, and p(positive|cancer) determines how much of that sector projects into the bottom bar, and the left sector of the bottom bar is p(positive&cancer).
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Similarly, if we know the number of patients with breast cancer and positive mammographies, and also the number of patients with breast cancer, we can estimate the chance that a woman with breast cancer gets a positive mammography by dividing: p(positive|cancer) = p(positive&cancer) / p(cancer).
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What about p(positive&cancer), p(positive&~cancer), p(~positive&cancer), and p(~positive&~cancer)? You might at first be tempted to think that there are only two degrees of freedom for these four quantities - that you can, for example, get p(positive&~cancer) by multiplying p(positive) * p(~cancer), and thus that all four quantities can be found given only the two quantities p(positive) and p(cancer). This is not the case! p(positive&~cancer) = p(positive) * p(~cancer) only if the two probabilities are statistically independent - if the chance that a woman has breast cancer has no bearing on whether she has a positive mammography.
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groups A, B, C, and D
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it follows that the entire set of 16 probabilities contains only three degrees of freedom. Remember that in our problems we always needed three pieces of information - the prior probability and the two conditional probabilities
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François ParmentierHere you will find an attempt to offer an intuitive explanation of Bayesian reasoning
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People do not employ Bayesian reasoning intuitively, find it very difficult to learn Bayesian reasoning when tutored, and rapidly forget Bayesian methods once the tutoring is over
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Bayesian problems in your dreams.
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the vast majority of doctors in these studies seem to have thought that if around 80% of women with breast cancer have positive mammographies, then the probability of a women with a positive mammography having breast cancer must be around 80%.
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a positive result on the mammography does increase the estimated probability,
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26 Dec 07
teateatea"What matters is that Bayes is cool, and if you don't know Bayes, you aren't cool."
development machinelearning math fun tool science statistics
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jeanjordaanWhy does a mathematical concept generate this strange enthusiasm in its students? What is the so-called Bayesian Revolution now sweeping through the sciences? Soon you will know. Soon you will be one of us.
academic algorithms article science statistics math bayes tutorial
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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.
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. istoyanovBayes' Theorem for the curious and bewildered; an excruciatingly gentle introduction. By Eliezer Yudkowsky
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Olifante *"science itself is a special case of Bayes' Theorem; experimental evidence is Bayesian evidence. [...] Karl Popper's falsificationism [...] is the old philosophy that the Bayesian revolution is currently dethroning."
bayesian statistics falsification evidence theorem conditional_probability
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Bayes' Theorem
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