posterior probability

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posterior probability

n
(Statistics) statistics the probability assigned to some parameter or to an event on the basis of its observed frequency in a sample, and calculated from a prior probability by Bayes' theorem. Compare prior probability See also empirical5
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To infer the posterior probability distribution of these, the conditional probability topic word [x.
From the priori probability and likelihood distribution theory, the weight's posterior probability distribution calculated by Bayes formula is:
Based on the ability for NN to be known as 100% fitting any function, we propose introducing NN to BN to calculate the posterior probability distribution of BN node under the evidence.
The basis for the Bayesian analysis of reporting period savings is the joint posterior probability distribution for the baseline regression parameter vector E and the variance [[sigma.
For AT1 transients, in contrast, there was a distinct peak in the posterior probability distribution for a change-point, and 97% of the posterior density for an abundance change occurred in the five years after 1989.
The 95% confidence interval of posterior probability distribution of the reaction rate constant includes the parameter estimate derived by the initial reaction rate method.
2]) can be measured by estimating the Bayesian posterior probability distribution of the parameters in the reparameterized model, where the posterior probability distribution is the probability distribution of the parameters given the data.
142], that this new probability distribution is the posterior probability distribution derived from the prior probability P by General Logical Imaging on y.
Using the Klepper model - but without the three irrelevant variables - and assigning to all of the parameters a normal prior probability with mean zero and a substantial variance, Scheines used Gibbs sampling to compute a posterior probability distribution for the lead-to-IQ parameter.
It seems almost truistic to say that the posterior probability distribution will be denser at c=2.
569 Table 2: Posterior Probability Distributions of Five Hospitals in Chicago under Two Models, the Hospital Compare Model (without Hospital Characteristics) and the Expanded Model (Including Hospital Characteristics of Volume, Nurse-to-Bed Ratio, Resident-to-Bed Ratio, and the Ability to Perform Cardiac Catheterization Procedures) Expanded Model (with Hospital Compare Model (without hospital characteristics) hospital characteristics) Credible Interval * Credible Interval Hospital Volume 2.
In a Bayesian analysis, prior distributions for the model parameters are combined with a likelihood function for the data to give posterior probability distributions for the parameters, from which inference is based (Gelman et al.

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