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 ?
2]), the posterior distributions are then in the same family as the prior probability distribution.
On the other hand, a narrow prior probability distribution can have a much higher or much lower probability for a particular effect size, depending on the details of the distribution and the specific effect.
In practice, the selection of a prior probability distribution is often substantially arbitrary.
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.
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.
In short, neither several researchers nor even the same researcher, in practice, will prefer one prior probability distribution to the exclusion of all others.
It is also assumed that whenever the prior probability distribution of expected losses of a risk is lognormal with median m, and an estimate is made of the expected losses, the joint probability distribution of the logarithms of the ratios (expected losses)/(estimate) and (expected losses)/m is bivariate normal.
Using the model, we derived the prior probability distributions of the h parameter for fish species that have a range of natural mortality, recruitment variabilities, and [N.
I for one would have liked to know more, for example, about the status of the constraints on prior probability distributions that characterize the `tempered personalism' endorsed by Schaffner; more about the (pretty desperate-sounding) suggestion that the problem of the `catch-all hypothesis' might be solved by interpreting probabilities as taking values not just in the real numbers simpliciter but in the reals augmented by Robinson's infinitesimals (all we get at present is that this suggestion, initially made by Jeffrey, `might' prove `a promising direction' p.
Translated into the language of beliefs, then, agents do not effortlessly come by prior probability distributions.
A major advantage of the Bayesian approach is the ability to include informative prior probability distributions for model parameters.