Fill in your details below or click an icon to log in: You are commenting using your account. The axioms of probability are these three conditions on the function P: 1. (P(S) = 100%. In fact it is the opposite of drunken rationale and takes you though a history of the development of randomness theory and the need for the evolutionary human brain to look for cause and effect patterns that are either not there, or that we misinterpret. The evidence would suggest that experts and amateurs alike are poor forecasters whether it comes to company earnings or macro events - it seems the future just isn't all that clear, whatever the scale! At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Example 1: Even if you are brilliant, you are not guaranteed to be admitted to Harvard: P(Admission|Brilliance) is low, because P(Admission) is low. I'm not saying I disagree, I'm just curious as to how you (or anyone else?) We write that the probability of the event is . Be able to organize the computation of conditional probabilities using trees and tables. On the other hand, with Sensitivity at 70% the probability of infection, given a negative test result, is not zero, but depends on the Base Rate. Conditional probability answers the question ‘how does the probability of an event change People tend to simply ignore the base rates, hence it is called (base rate neglect). The theorem concerns the incorporation of new information into old, in order to accurately determine the revised probability of an event in light of the new information. It shows how a prior assumption (called prior probability) is updated in a light of new evidence. My own experience is that it has several times been possible to call the oil sector and to position oneself with advantage. Thanks for the book recommendation, had a quick look on Amazon and it looks like an interesting read. He says this is a way of limiting the size of his loss if he has made a bad selection of a particular stock, thereby preserving capital for better use elsewhere. Conclusion By the way, I thought that what you said here: Now you have pointed it out it it seems blindingly obvious! When evaluating the probability of an event―for instance, diagnosing a disease, there are two types of information that may be available. … Base rate fallacy/false positive paradox is derived from Bayes theorem. Bayes’ theorem: what it is, a simple example, and a counter-intuitive example that demonstrates the base rate fallacy. Conclusion5. We will begin to justify this view today. Our intuition about what is, or is not evidence, and what is strong versus weak evidence, can be terribly wrong (see, for instance, the base rate fallacy). - He prefers 'family-run' companies in which the directors have large shareholdings themselves, have 'clean' reputations and have an attitude of being 'stewards' of their shareholders money. When the incidence of a disease in a population is low, unless the test … Lets see how that looks like, by comparing a rare disease (Multiple sclerosis) with a more common disease (lactose intolerance, technically not a disease). Birn-baum showed that behavior described as "ne-glect of base rate" may be consistent with ra-tional Bayesian utilization of the base rate. Behavioral and brain sciences, 19(1), 1-17. A generic information about how frequently an event occurs naturally. I also recommend: Reminisences of a Stockmarket Trader,  One up on Wall St and Where are the Customers Yachts, in particular. Change ), You are commenting using your Facebook account. - He looks for established companies with a record of profitability and dividend payments. However, to do that, we need to include the possibility that we could be one of the rare false positives. If Hand Dare events, then: P(P(HjD) = DjH)P(H) P(D) Our view is that Bayes’ theorem forms the foundation for inferential statistics. If so, why? Why do knowers of Bayes's Theorem still commit the Base Rate Fallacy? P(E|H) is the probability of the evidence if the hypothesis is true. the proportion of those who have a given condition, is lower than the test’s false positive rate, even tests that have a very low chance of giving a false positive in an individual case will give more false than … I do not claim any generalised success in other sectors but I'm working on it. Bayes’ theorem was developed by Rev. Despite John’s appearance increasing the probability that he considers himself a Satanist, the fact is that there are around 2 billion Christians in the world and very few Satanists. But it is frequently possible to get a bearing on just one or two sectors - banks, oil companies, house builders and to act accordingly without having to complement that insight by picking the top performing individual stocks. Such a statement would be so broad and so nebulous as to be of no value. If we look at the investment process through this probabilistic lens, what can consideration of base rates and Bayes’ theorem offer us? If so, why? Applications and examples. One great example of the Bayes theorem and how it impacts our daily decision making is the base rate fallacy. The so-called Bayes Rule or Bayes Formula is useful when trying to interpret the results of diagnostic tests with known or estimated population-level prevalence, e.g. [This must improve his stock-picking success rate, although it is hard to quantify this, and many private investors don't have the time to do this.]. Very interesting read. Change ), You are commenting using your Google account. One night, a cab is involved in a hit and run accident. A witness claims the cab was green, however later tests show that they only correctly … Suppose you came to the realisation that the oil sector was poised to outperform. [It is well known that 'value' stocks and stocks with high dividend yields tend as a group to out-perform over the long-run.] You are told that “John is a man who wears gothic inspired clothing, has long black hair, and listens to death metal.”  You are then asked “How likely is it that he is a Christian, and how likely is it that he is a Satanist?”. - He tends to buy stocks of small, rather than big, companies. These are most easily described and understood with an example, which I have shamelessly sourced from Wikipedia. After that, the servant threw other balls on the same table and was ask to tell Bayes, where this (second, third, fourth…) ball has fallen in relationship to the mark of the first ball. All the best, I'd look at things from a different angle. Bayesian inference includes conditional probability. If I was to employ such a strategy, my worry would be that I've essentially replaced one forecasting problem (the stock picking problem) with another almost identical forecasting problem (the sector picking problem). View all posts by kilian. Interesting what you say about picking sectors, it makes sense in the Bayesian context and the house builders you mention are quite a good example. [Again, this reduces the chances of fraud by the management at the expense of shareholders.] Base rate fallacy, or base rate neglect, is a cognitive error whereby too little weight is placed on the base, or original rate, of possibility (e.g., the probability of A given B).
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