Statement of theorem
Bayes' theorem is stated mathematically as the following equation: where and are events and . * is a conditional probability: the probability of event occurring given that is true. It is also called theProof
For events
Bayes' theorem may be derived from the definition of conditional probability: : where is the probability of both A and B being true. Similarly, : Solving for and substituting into the above expression for yields Bayes' theorem: :For continuous random variables
For two continuous random variables ''X'' and ''Y'', Bayes' theorem may be analogously derived from the definition ofGeneral case
Let be the conditional distribution of given and let be the distribution of . The joint distribution is then . The conditional distribution of given is then determined by Existence and uniqueness of the neededExamples
Recreational mathematics
Bayes' rule and computing conditional probabilities provide a solution method for a number of popular puzzles, such as the Three Prisoners problem, theDrug testing
Suppose, a particular test for whether someone has been using cannabis is 90% sensitive, meaning the true positive rate (TPR) = 0.90. Therefore, it leads to 90% true positive results (correct identification of drug use) for cannabis users. The test is also 80% specific, meaning true negative rate (TNR) = 0.80. Therefore, the test correctly identifies 80% of non-use for non-users, but also generates 20% false positives, or false positive rate (FPR) = 0.20, for non-users. Assuming 0.05 prevalence, meaning 5% of people use cannabis, what is theSensitivity or specificity
The importance of specificity can be seen by showing that even if sensitivity is raised to 100% and specificity remains at 80%, the probability of someone testing positive really being a cannabis user only rises from 19% to 21%, but if the sensitivity is held at 90% and the specificity is increased to 95%, the probability rises to 49%.Cancer rate
Even if 100% of patients with pancreatic cancer have a certain symptom, when someone has the same symptom, it does not mean that this person has a 100% chance of getting pancreatic cancer. Assuming the incidence rate of pancreatic cancer is 1/100000, while 10/99999 healthy individuals have the same symptoms worldwide, the probability of having pancreatic cancer given the symptoms is only 9.1%, and the other 90.9% could be "false positives" (that is, falsely said to have cancer; "positive" is a confusing term when, as here, the test gives bad news). Based on incidence rate, the following table presents the corresponding numbers per 100,000 people. Which can then be used to calculate the probability of having cancer when you have the symptoms: :Defective item rate
A factory produces items using three machines—A, B, and C—which account for 20%, 30%, and 50% of its output respectively. Of the items produced by machine A, 5% are defective; similarly, 3% of machine B's items and 1% of machine C's are defective. If a randomly selected item is defective, what is the probability it was produced by machine C? Once again, the answer can be reached without using the formula by applying the conditions to a hypothetical number of cases. For example, if the factory produces 1,000 items, 200 will be produced by Machine A, 300 by Machine B, and 500 by Machine C. Machine A will produce 5% × 200 = 10 defective items, Machine B 3% × 300 = 9, and Machine C 1% × 500 = 5, for a total of 24. Thus, the likelihood that a randomly selected defective item was produced by machine C is 5/24 (~20.83%). This problem can also be solved using Bayes' theorem: Let ''Xi'' denote the event that a randomly chosen item was made by the ''i'' th machine (for ''i'' = A,B,C). Let ''Y'' denote the event that a randomly chosen item is defective. Then, we are given the following information: : If the item was made by the first machine, then the probability that it is defective is 0.05; that is, ''P''(''Y'' , ''X''A) = 0.05. Overall, we have : To answer the original question, we first find ''P''(Y). That can be done in the following way: : Hence, 2.4% of the total output is defective. We are given that ''Y'' has occurred, and we want to calculate the conditional probability of ''X''C. By Bayes' theorem, : Given that the item is defective, the probability that it was made by machine C is 5/24. Although machine C produces half of the total output, it produces a much smaller fraction of the defective items. Hence the knowledge that the item selected was defective enables us to replace the prior probability ''P''(''X''C) = 1/2 by the smaller posterior probability ''P''(XC , ''Y'') = 5/24.Interpretations
The interpretation of Bayes' rule depends on the interpretation of probability ascribed to the terms. The two main interpretations are described below. Figure 2 shows a geometric visualization.Bayesian interpretation
In the Bayesian (or epistemological) interpretation, probability measures a "degree of belief". Bayes' theorem links the degree of belief in a proposition before and after accounting for evidence. For example, suppose it is believed with 50% certainty that a coin is twice as likely to land heads than tails. If the coin is flipped a number of times and the outcomes observed, that degree of belief will probably rise or fall, but might even remain the same, depending on the results. For proposition ''A'' and evidence ''B'', * ''P'' (''A''), the ''prior'', is the initial degree of belief in ''A''. * ''P'' (''A'' , ''B''), the ''posterior'', is the degree of belief after incorporating news that ''B'' is true. * the quotient represents the support ''B'' provides for ''A''. For more on the application of Bayes' theorem under the Bayesian interpretation of probability, seeFrequentist interpretation
In the frequentist interpretation, probability measures a "proportion of outcomes". For example, suppose an experiment is performed many times. ''P''(''A'') is the proportion of outcomes with property ''A'' (the prior) and ''P''(''B'') is the proportion with property ''B''. ''P''(''B'' , ''A'') is the proportion of outcomes with property ''B'' ''out of'' outcomes with property ''A'', and ''P''(''A'' , ''B'') is the proportion of those with ''A'' ''out of'' those with ''B'' (the posterior). The role of Bayes' theorem is best visualized with tree diagrams such as Figure 3. The two diagrams partition the same outcomes by ''A'' and ''B'' in opposite orders, to obtain the inverse probabilities. Bayes' theorem links the different partitionings.Example
AnForms
Events
Simple form
For events ''A'' and ''B'', provided that ''P''(''B'') ≠ 0, : In many applications, for instance inAlternative form
Another form of Bayes' theorem for two competing statements or hypotheses is: : For an epistemological interpretation: For proposition ''A'' and evidence or background ''B'', * is the prior probability, the initial degree of belief in ''A''. * is the corresponding initial degree of belief in ''not-A'', that ''A'' is false, where * is the conditional probability or likelihood, the degree of belief in ''B'' given that proposition ''A'' is true. * is the conditional probability or likelihood, the degree of belief in ''B'' given that proposition ''A'' is false. * is theExtended form
Often, for some partition of the sample space, the event space is given in terms of ''P''(''Aj'') and ''P''(''B'' , ''Aj''). It is then useful to compute ''P''(''B'') using the law of total probability: : : In the special case where ''A'' is a binary variable: :Random variables
Consider a sample space Ω generated by two random variables ''X'' and ''Y''. In principle, Bayes' theorem applies to the events ''A'' = and ''B'' = . : However, terms become 0 at points where either variable has finite probability density. To remain useful, Bayes' theorem must be formulated in terms of the relevant densities (see Derivation).Simple form
If ''X'' is continuous and ''Y'' is discrete, : where each is a density function. If ''X'' is discrete and ''Y'' is continuous, : If both ''X'' and ''Y'' are continuous, :Extended form
A continuous event space is often conceptualized in terms of the numerator terms. It is then useful to eliminate the denominator using the law of total probability. For ''fY''(''y''), this becomes an integral: :Bayes' rule in odds form
Bayes' theorem in odds form is: : where : is called the Bayes factor or likelihood ratio. The odds between two events is simply the ratio of the probabilities of the two events. Thus : : Thus, the rule says that the posterior odds are the prior odds times the Bayes factor, or in other words, the posterior is proportional to the prior times the likelihood. In the special case that and , one writes , and uses a similar abbreviation for the Bayes factor and for the conditional odds. The odds on is by definition the odds for and against . Bayes' rule can then be written in the abbreviated form : or, in words, the posterior odds on equals the prior odds on times the likelihood ratio for given information . In short, posterior odds equals prior odds times likelihood ratio. For example, if a medical test has a sensitivity of 90% and a specificity of 91%, then the positive Bayes factor is . Now, if the prevalence of this disease is 9.09%, and if we take that as the prior probability, then the prior odds is about 1:10. So after receiving a positive test result, the posterior odds of actually having the disease becomes 1:1; In other words, the posterior probability of actually having the disease is 50%. If a second test is performed in serial testing, and that also turns out to be positive, then the posterior odds of actually having the disease becomes 10:1, which means a posterior probability of about 90.91%. The negative Bayes factor can be calculated to be 91%/(100%-90%)=9.1, so if the second test turns out to be negative, then the posterior odds of actually having the disease is 1:9.1, which means a posterior probability of about 9.9%. The example above can also be understood with more solid numbers: Assume the patient taking the test is from a group of 1000 people, where 91 of them actually have the disease (prevalence of 9.1%). If all these 1000 people take the medical test, 82 of those with the disease will get a true positive result (sensitivity of 90.1%), 9 of those with the disease will get a false negative result ( false negative rate of 9.9%), 827 of those without the disease will get a true negative result (specificity of 91.0%), and 82 of those without the disease will get a false positive result (false positive rate of 9.0%). Before taking any test, the patient's odds for having the disease is 91:909. After receiving a positive result, the patient's odds for having the disease is : which is consistent with the fact that there are 82 true positives and 82 false positives in the group of 1000 people.Correspondence to other mathematical frameworks
Propositional logic
Using twice, one may use Bayes' theorem to also express in terms of and without negations: :, when . From this we can read off the inference :. In words: If certainly implies , we infer that certainly implies . Where , the two implications being certain are equivalent statements. In the probability formulas, the conditional probability generalizes the logical implication , where now beyond assigning true or false, we assign probability values to statements. The assertion of is captured by certainty of the conditional, the assertion of . Relating the directions of implication, Bayes' theorem represents a generalization of the contraposition law, which in classical propositional logic can be expressed as: :. Note that in this relation between implications, the positions of resp. get flipped. The corresponding formula in terms of probability calculus is Bayes' theorem, which in its expanded form involving the prior probability/ base rate of only , is expressed as: :.Subjective logic
Bayes' theorem represents a special case of deriving inverted conditional opinions in subjective logic expressed as: : where denotes the operator for inverting conditional opinions. The argument denotes a pair of binomial conditional opinions given by source , and the argument denotes the prior probability (aka. the base rate) of . The pair of derivative inverted conditional opinions is denoted . The conditional opinion generalizes the probabilistic conditional , i.e. in addition to assigning a probability the source can assign any subjective opinion to the conditional statement . A binomial subjective opinion is the belief in the truth of statement with degrees of epistemic uncertainty, as expressed by source . Every subjective opinion has a corresponding projected probability . The application of Bayes' theorem to projected probabilities of opinions is aGeneralizations
Conditioned version
A conditioned version of the Bayes' theorem results from the addition of a third event on which all probabilities are conditioned: :Derivation
Using the chain rule : And, on the other hand : The desired result is obtained by identifying both expressions and solving for .Bayes' rule with 3 events
In the case of 3 events - A, B, and C - it can be shown that:History
Bayes' theorem is named after the Reverend Thomas Bayes (; c. 1701 – 1761), who first used conditional probability to provide an algorithm (his Proposition 9) that uses evidence to calculate limits on an unknown parameter, published as '' An Essay towards solving a Problem in the Doctrine of Chances'' (1763). He studied how to compute a distribution for the probability parameter of a binomial distribution (in modern terminology). On Bayes's death his family transferred his papers to his old friend, Richard Price (1723–1791) who over a period of two years significantly edited the unpublished manuscript, before sending it to a friend who read it aloud at theUse in genetics
In genetics, Bayes' theorem can be used to calculate the probability of an individual having a specific genotype. Many people seek to approximate their chances of being affected by a genetic disease or their likelihood of being a carrier for a recessive gene of interest. A Bayesian analysis can be done based on family history or genetic testing, in order to predict whether an individual will develop a disease or pass one on to their children. Genetic testing and prediction is a common practice among couples who plan to have children but are concerned that they may both be carriers for a disease, especially within communities with low genetic variance. The first step in Bayesian analysis for genetics is to propose mutually exclusive hypotheses: for a specific allele, an individual either is or is not a carrier. Next, four probabilities are calculated: Prior Probability (the likelihood of each hypothesis considering information such as family history or predictions based on Mendelian Inheritance), Conditional Probability (of a certain outcome), Joint Probability (product of the first two), and Posterior Probability (a weighted product calculated by dividing the Joint Probability for each hypothesis by the sum of both joint probabilities). This type of analysis can be done based purely on family history of a condition or in concert with genetic testing.Using pedigree to calculate probabilities
Example of a Bayesian analysis table for a female individual's risk for a disease based on the knowledge that the disease is present in her siblings but not in her parents or any of her four children. Based solely on the status of the subject's siblings and parents, she is equally likely to be a carrier as to be a non-carrier (this likelihood is denoted by the Prior Hypothesis). However, the probability that the subject's four sons would all be unaffected is 1/16 (···) if she is a carrier, about 1 if she is a non-carrier (this is the Conditional Probability). The Joint Probability reconciles these two predictions by multiplying them together. The last line (the Posterior Probability) is calculated by dividing the Joint Probability for each hypothesis by the sum of both joint probabilities.Using genetic test results
Parental genetic testing can detect around 90% of known disease alleles in parents that can lead to carrier or affected status in their child. Cystic fibrosis is a heritable disease caused by an autosomal recessive mutation on the CFTR gene, located on the q arm of chromosome 7."CFTR Gene – Genetics Home Reference". U.S. National Library of Medicine, National Institutes of Health, ghr.nlm.nih.gov/gene/CFTR#location. Bayesian analysis of a female patient with a family history of cystic fibrosis (CF), who has tested negative for CF, demonstrating how this method was used to determine her risk of having a child born with CF: Because the patient is unaffected, she is either homozygous for the wild-type allele, or heterozygous. To establish prior probabilities, a Punnett square is used, based on the knowledge that neither parent was affected by the disease but both could have been carriers: Given that the patient is unaffected, there are only three possibilities. Within these three, there are two scenarios in which the patient carries the mutant allele. Thus the prior probabilities are and . Next, the patient undergoes genetic testing and tests negative for cystic fibrosis. This test has a 90% detection rate, so the conditional probabilities of a negative test are 1/10 and 1. Finally, the joint and posterior probabilities are calculated as before. After carrying out the same analysis on the patient's male partner (with a negative test result), the chances of their child being affected is equal to the product of the parents' respective posterior probabilities for being carriers times the chances that two carriers will produce an affected offspring ().Genetic testing done in parallel with other risk factor identification.
Bayesian analysis can be done using phenotypic information associated with a genetic condition, and when combined with genetic testing this analysis becomes much more complicated. Cystic Fibrosis, for example, can be identified in a fetus through an ultrasound looking for an echogenic bowel, meaning one that appears brighter than normal on a scan2. This is not a foolproof test, as an echogenic bowel can be present in a perfectly healthy fetus. Parental genetic testing is very influential in this case, where a phenotypic facet can be overly influential in probability calculation. In the case of a fetus with an echogenic bowel, with a mother who has been tested and is known to be a CF carrier, the posterior probability that the fetus actually has the disease is very high (0.64). However, once the father has tested negative for CF, the posterior probability drops significantly (to 0.16). Risk factor calculation is a powerful tool in genetic counseling and reproductive planning, but it cannot be treated as the only important factor to consider. As above, incomplete testing can yield falsely high probability of carrier status, and testing can be financially inaccessible or unfeasible when a parent is not present.See also
*Notes
References
Bibliography
*Further reading
* * Gelman, A, Carlin, JB, Stern, HS, and Rubin, DB (2003), "Bayesian Data Analysis," Second Edition, CRC Press. * Grinstead, CM and Snell, JL (1997), "Introduction to Probability (2nd edition)," American Mathematical Society (free pdf availableExternal links
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