# A Priori Probability

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## A Priori Probability

In statistics, the use of logic to estimate the probability of an event. For example, when considering a company's earnings, the company can make a profit, suffer a loss, or break even in a given year. All other things being equal, there is a 1/3 a priori probability of each scenario occurring.
References in periodicals archive ?
If users submit a new training sample [T'.sub.k] and merge it with the original training sample Tk, the prior probability P[(u,v,w) S[C.sub.k]] shall be re-calculated through Formula (16):
We could specify the reporting probability using additional parameters in these works, for instance, the predefined prior probability threshold [sigma] in .
According to the definition of the LR, any scenario given a nonzero prior probability by the DM can influence the value of the LR and is therefore relevant; scenarios given a prior probability of zero cannot influence the value of the LR regardless of the value of the corresponding likelihood Pr[y|x, [H.sub.j]], j = 1,..., N, and are therefore irrelevant to the DM.
The results in Tables 4 and 5 indicate that the prior probability of occurrence of a landslide (surge) in the reservoir is 0.0387.
(2) P([D.sub.i]) is the prior probability of [D.sub.i] (the prior probability of occurrence of disease);
Contrary to Johnson, assessing the prior probability that the database
A probability model comprising prior probability and likelihood can be trained through practical application using Bayesian updating.
Since the sum of all the elements in x is 1 and its ith element [[omega].sub.i] represents the relative importance of the state [S.sub.i] among all the states, it is natural to interpret [[omega].sub.i] as the prior probability of stat [S.sub.i].
More generally, assume that there is a prior probability P([[omega].sub.k]) of each subclass k.
The input quantities for Bayesian inference are on the left-hand side of (1) and are as follows: p([theta] | I) is the prior probability for a hypothesis about the values of the source parameter vector [theta] which encodes our state of knowledge about these parameters before the receipt of the concentration data d and p(d | [theta],1) is the likelihood function and is considered to be a function of [theta] for fixed data d.
The Obtaining of the Prior Probability Density Function Based on Noise Level Estimation.
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