A formula that is used to update the probability of a given event, given new information that supplements the preexisting base rate associated with the event in question. go to glossary index

This is a simple formula that says that if a particular test result is twice as likely to occur in patients with a disease, condition, or injury than in patients without, then, it is twice as likely that the patient with the result being tested for actually has the disease as compared to any randomly selected similar patient who has not been tested. If you don't like thinking about things like this, just use the nomogram in the users guides or the calculator on the diagnosis appraisal page.

(statistics) a theorem describing how the conditional probability of a set of possible causes for a given observed event can be computed from knowledge of the probability of each cause and the conditional probability of the outcome of each cause

Combining the prior and conditional probabilities of certain events or the results of specific tests to give a joint probability to derive the posterior or relative probability.

The classic statistical approach to implement probabalistic reasoning in AI applications.

Theorem used to calculate the relative probability of an event given the probabilities of associated events. Used to calculate the probability of a disease given the frequencies of symptoms and signs within the disease and within the normal population. See also: Conditional probability, Prior Probability, Posterior Probability.

A statistical procedure to assess the relative probability of two alternative possibilities based on acquired information.(CPI) (Pr) (CPI) (Pr) + (1 - Pr) Probability of Paternity CPI = Combined Paternity Index Pr = Prior Probability

A formula for calculating conditional probabilities or recalculating probabilities based on additional information. Bayes' Theorem is used for inferential and predictive reasoning in Artificial Intelligence and predictive routing in Networking.

Bayes' theorem (also known as Bayes' rule or Bayes' law) is a result in probability theory, which relates the conditional and marginal probability distributions of random variables. In some interpretations of probability, Bayes' theorem tells how to update or revise beliefs in light of new evidence: a posteriori.