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 ?
One of the great challenges in Bayesian inference is to find an adequate prior probability distribution. This distribution is obtained from past information and expresses the beliefs about the probability of the studied event before some evidence is taken into account.
Since the conjugate prior is used for ([mu], [[sigma].sup.2]), the posterior distributions are then in the same family as the prior probability distribution. Therefore, assuming a conjugate prior N-Inv-[chi square] ([[mu].sub.0], [[sigma].sup.2.sub.0]/[[kappa].sub.0], [[upsilon].sub.0], [[sigma].sup.2.sub.0]) for ([mu], [[sigma].sup.2]), then the posterior distribution of ([mu], [[sigma].sup.2]) is the N-Inv-[chi square] ([[mu].sub.t], [[sigma].sup.2.sub.t]/[[kappa].sub.t], [[upsilon].sub.t], [[sigma].sup.2.sub.t]).
The second case is that B does not know enough about C's portfolio weights, and B's beliefs cannot be described by a unique prior probability distribution. Instead, B may be uncertain about the portfolio weights and thus may hold multiple priors over the weights.
In order to be able to apply imaging in this context, we also have to assume the presence of a prior probability distribution P on the term space, assigning to each term t [is an element of] T a probability P(t) so that [summation of]tP(t) = 1.
The precise posterior arrived at here is not the issue, since it can be manipulated at will by adjusting the prior probability distribution over the hypotheses.
Also, a prior probability distribution of the change-point and a joint density function of the sample mean and sample standard deviation have to be determined by the user in the light of the knowledge of a particular application.
In the normal regression case that we are concerned with, this prior probability distribution can be summarized by prior means and variances for the regression coefficients.
Assume a prior probability distribution of expected losses for a risk, relative to certain information.
It is beyond the scope of this editorial to describe the mathematical challenges when defining prior probability distributions and then combining those distributions with the likelihoods of new data.