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 ?
HEQ will pay a distribution of USD0.3760 per share, unchanged from the prior distribution; JHI will pay USD0.3246 per share, increased from that paid previously; JHS will pay a distribution of USD0.1550 per share, up from the earlier distribution; HTY will pay USD0.1600 per share; and BTO will pay a distribution of USD0.5500 per share, both unchanged from the earlier payments.
BayesA, BayesB, BayesC[pi] and their extended forms assume that the prior distribution of variances of SNP non-zero effects is a scaled inverse chi-square distribution with degree of freedom and a scale parameter sg [15].
To conduct these analyses, we used the prior distribution with r = 0.5 because it is the suggested default distribution by Rouder et al.
Increased the first quarter dividend payment by 4 percent from the prior distribution representing the fifth time JLL Income Property Trust has raised its dividend since 2012.
(5), Bayesian inference derives a posterior distribution (P( x | Y)) of variables (x), offsetting constants, based on a likelihood function (P(Y | x)) and a prior distribution of the variables ([pi](x)).
The first step in applying the Bayesian inference was to determine the prior distribution that denotes the beliefs about the variable before some evidence is taken into account.
Benefiting from the successful application of the Markov chain Monte Carlo (MCMC) methods in Bayesian inference, the choice of prior distribution is not limited within conjugate distributions and the modelling to multitype data has been significantly extended.
However, if the width of the likelihood distribution is too small in comparison to the width of the prior distribution or if measurements are located in the tail of the prior distribution, this choice may fail; see [4].
Based on the historical degradation data and the historical failure time data, the prior distribution of the model parameters was estimated by using the expectation maximization (EM) algorithm.
It considers prior information and the optimal prior distribution is selected by the maximum entropy under the boundary conditions [16, 17].
We divide TX, CA, and POIs into 8km x 8km regions and calculate a prior distribution of query contents for each region.
Here, P([phi]) is the prior distribution of the individual model parameters and hyperparameters, P(D|[phi]) is the likelihood, and P(D) can be treated as a normalization factor.