prior probability

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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


(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.
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.
Segen's Medical Dictionary. © 2012 Farlex, Inc. All rights reserved.

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 ?
(5.) Notice that the same would be true even if the agents had an uninformative prior. In that case, the conditional expectation of [y.sup.i] would be exactly equal to [s.sup.i.sub.j].
We choose the uninformative prior as n = (k/p, ..., k/p)' with k = 20.
In the uninformative prior setting the model selects about 11% of the true predictors.
The data2 was used for comparing the classical method with Bayesian method with uninformative priors and informative priors.
This shows that the use of an informative prior is more paying than an uninformative prior. The extent of over or under-estimation is reduced with the increase in sample size.
Other uninformative priors were tried with no significant alteration in the results presented in Table 3.
The comparison of the informative and non-informative priors with respect to posterior variance, the Bayesian interval estimate, the coefficient of skewness of the posterior distribution and the Bayes posterior risk shows that the informative prior is more advantageous than the uninformative prior.
The differences between informative and uninformative priors in absolute bias and RMSE were lower in the large sample condition, but recovery was still better for the informative prior.
In general, the Bayesian estimator collapses to Maximum Likelihood with uninformative priors. (We discuss prior elucidation for our fiscal application in the subsequent section.)
Relatively uninformative priors were used for [[phi].sub.1] and [[phi].sub.2] (Fig.
Because posterior model probabilities are sensitive to the priors placed on model coefficients (Link and Barker 2010), we conducted a prior sensitivity analysis by placing 3 different uninformative priors on model coefficients (N(0,10), N(0,31.6), and N(0,100)).
An extensive simulation study is conducted to highlight some interesting properties of the Bayes estimates of the proposed Burr mixture assuming conjugate and uninformative priors. A real life application of the proposed mixture is presented as well.