词条 | Chain rule (probability) |
释义 |
In probability theory, the chain rule (also called the general product rule{{sfn|Schum|1994}}{{sfn|Klugh|2013}}) permits the calculation of any member of the joint distribution of a set of random variables using only conditional probabilities. The rule is useful in the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities. Chain rule for eventsTwo eventsThe chain rule for two random events and says . ExampleThis rule is illustrated in the following example. Urn 1 has 1 black ball and 2 white balls and Urn 2 has 1 black ball and 3 white balls. Suppose we pick an urn at random and then select a ball from that urn. Let event be choosing the first urn: . Let event be the chance we choose a white ball. The chance of choosing a white ball, given that we have chosen the first urn, is . Event would be their intersection: choosing the first urn and a white ball from it. The probability can be found by the chain rule for probability: . More than two eventsFor more than two events the chain rule extends to the formula which by induction may be turned into . ExampleWith four events (), the chain rule is Chain rule for random variablesTwo random variablesFor two random variables , to find the joint distribution, we can apply the definition of conditional probability to obtain: More than two random variablesConsider an indexed collection of random variables . To find the value of this member of the joint distribution, we can apply the definition of conditional probability to obtain: Repeating this process with each final term creates the product: ExampleWith four variables (), the chain rule produces this product of conditional probabilities: FootnotesReferences
3 : Probability theory|Bayesian inference|Bayesian statistics |
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