# prior probability

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Related to Prior probabilities: Posterior probabilities, Uninformative prior

## pri·or prob·a·bil·i·ty

the best rational assessment of the probability of an outcome on the basis of established knowledge before the present experiment is performed. For instance, the prior probability of the daughter of a carrier of hemophilia being herself a carrier of hemophilia is 1/2. But if the daughter already has an affected son, the posterior probability that she is a carrier is unity, whereas if she has a normal child, the posterior probability that she is a carrier is 1/3. See: Bayes theorem.
Farlex Partner Medical Dictionary © Farlex 2012

## prevalence

Epidemiology
(1) The number of people with a specific condition or attribute at a specified time divided by the total number of people in the population.
(2) The number or proportion of cases, events or conditions in a given population.

Statistics
A term defined in the context of a 4-cell diagnostic matrix (2 X 2 table) as the amount of people with a disease, X, relative to a population.

Veterinary medicine
(1) A clinical estimate of the probability that an animal has a given disease, based on current knowledge (e.g., by history of physical exam) before diagnostic testing.
(2) As defined in a population, the probability at a specific point in time that an animal randomly selected from a group will have a particular condition, which is equivalent to the proportion of individuals in the group that have the disease. Group prevalence is calculated by dividing the number of individuals in a group that have a disease by the total number of individuals in the group at risk of the disease. Prevalence is a good measure of the amount of a chronic, low-mortality disease in a population, but is not of the amount of short duration or high-fatality disease. Prevalence is often established by cross-sectional surveys.

## prior probability

Decision making The likelihood that something may occur or be associated with an event based on its prevalence in a particular situation. See Medical mistake, Representative heurisic.
McGraw-Hill Concise Dictionary of Modern Medicine. © 2002 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
In the process of learning congestion prior probabilities of links in IP network, we can obtain t number static BNMs if the routing matrix D changes t times.
If it turns out that these two numbers perfectly coincide, then we do not update the prior probabilities. If the number of actual matches is large relative to the predicted number, then this makes it more likely that the match was a true match.
Prior probabilities according to test sequences Genre Seq.
As mentioned previously, the job of the language model in a speech recognizer is to assess the prior probabilities P(W of potential recognized word sequences.
Analogously, they claim, in the case of scientific reasoning, whether deductive or inductive, we should not be concerned with the legitimacy of the starting points, that is, the prior probabilities, but only with the means by which conclusions are derived from them.
The prior probabilities for each person within that large set are plainly not all the same; people who live near the crime or who knew the victim or who had some apparent motive to commit the crime are generally more likely than others to have done it.
Therefore, comparing the outcomes of treatments for patients depends on fairly comparing patients' prior probabilities of the outcomes under study before treatments are introduced.
If the prior probabilities of test experiments are fixed as E[p([h.sub.k])] = [p.sub.k] (0 [less than or equal to] [p.sub.k] [less than or equal to] 1) and populations N([[mu].sub.k], [[summation].sub.k]) are independent, then the mean and variance estimates of mixed population f(x, z) will be
An attacker could use the prior probabilities to try to compromise the secrecy.
They showed that even unprejudiced physicians would be forced to rely on prior probabilities to a greater degree when treating black patients than white patients.
(This association is not surprising, given that signal detection theory can be treated as a special form of statistical decision theory with additional parametric assumptions.) [[beta].sub.opt] is the threshold at which, if for larger values one decides S (signal) and for smaller values one decides N (noise), one maximizes expected return (based on given contingent costs, benefits, and prior probabilities of F).

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