prior probability

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prior probability

n
(Statistics) statistics the probability assigned to a parameter or to an event in advance of any empirical evidence, often subjectively or on the assumption of the principle of indifference. Compare posterior probability
References in periodicals archive ?
This dividend is up by 4% from its prior distribution for the fourth quarter 2017.
0,j] was estimated separately for each dataset j, as there was some apparent drift in the normalization, with a normal prior distribution across datasets j of [[theta].
He noted that the authors' robustness tests did not illustrate this point, and he suspected that the authors had a prior distribution in which the innovations to the natural rate of interest had small variances, implying that a slowly declining trend would emerge from their filtering of the data, but that the data did not actually inform the statistical process, and hence the degree of smoothing by the filter, in a meaningful way.
Under the terms of a prior distribution agreement with Ferring, Apricus has received a total of USD4.
where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is the probability of the observed data conditional on all parameters, f (a, d,[sigma]) is the prior distribution and f([theta]) is a multivariate normal density function.
Prior distribution practices had involved striking 50, maybe 60 prints which would circulate in Germany regionally.
The joint prior distribution will therefore be given by [pi]([theta], [PSI]) (Ardia and Hoogerheide, 2010).
Considering the boundary regions are more likely to be background elements, the color contrast is used to extract the high-contrast region as the foreground seeds, and we utilize these seeds to obtain the prior distribution, which is similar to the idea in [3], as the basis of the following processes.
Furthermore, the Bayesian estimator of CL on the basis of the conjugate Gamma prior distribution is also obtained under squared error loss and LINEX loss functions.
More generally, we say a prior distribution is "almost uninformative" (or more rigorously, "not very informative") if it is close to a flat prior.
The prior distribution of all effects are multivariate normals
2]) which is the probability that both of the models are valid; similarly, the second and third terms contain the posterior distribution [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] using the calibration data at Level i alone and its weight is the probability that the model at Level i is valid but the model at another level is invalid; the last term contains the prior distribution f([theta]) with the weight P(G.