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.


(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.

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.

prior probability,

n the extent of belief held by a patient and practitioner in the ability of a specific therapeutic approach to produce a positive outcome before treatment begins. This level of belief should be taken into consideration by the patient and practitioner to make a decision as to whether the treatment should be used or to permit the therapy to continue.


the basis of statistics. The relative frequency of occurrence of a specific event as the outcome of an experiment when the experiment is conducted randomly on very many occasions. The probability of the event occurring is the number of times it did occur divided by the number of times that it could have occurred. Defined as:$$\hbox{p}={\hbox{x}\over (\hbox{x+y})$$

p = probability, x = positive outcomes, y = negative outcomes.
prior probability
estimation of the probability that a particular phenomenon or character will appear before putting the patient to the test, e.g. testing the probable productivity of a patient by testing its forebears.
subjective probability
the measure of the assessor's belief in the probability of a proposition being correct.
References in periodicals archive ?
The prior distribution of all parameters was set to a normal distribution with a mean of zero and variance of [10.
23) A gamma prior distribution is specified for parameter R, to represent the uncertainty regarding the true attrition rates.
Practically speaking, an even larger advantage is the ability to incorporate practice wisdom into the analysis in the form of the prior distribution.
The prior distribution of [mu] is chosen to be a conjugated multivariate normal distribution [N.
It is the expectation of L(E | [phi]) with respect to prior distribution [[pi].
2] a non-informative prior distribution and obtain the posterior distribution of [mu] by Markov Chain Monte Carlo methods.
A more flexible and conjugate prior distribution for the model (3)-(4) is based on the following observation.
The weights for this average are approximately (or exactly, in the Gaussian case) inversely proportional to the respective variances of the prior distribution and the maximum likelihood estimator.
The Bayesian approach treats the parameters of the model as random variables and requires that prior distributions be specified for them; these prior distribution are denoted by p([theta]) in Bayes's theorem.