posterior distribution


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posterior distribution

[pä¦stir·ē·ər ‚dis·trə′byü·shən]
(statistics)
A probability distribution on the values of an unknown parameter that combines prior information about the parameter contained in the observed data to give a composite picture of the final judgments about the values of the parameter.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.
References in periodicals archive ?
The first table reports the median from the simulated distribution of moments using samples generated with parameter draws of the posterior distribution, while the second simulates the model using the posterior median of the estimated parameters.
Thus, in general, the posterior distribution is computed numerically using computer software.
its posterior distribution. [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]
We begin by explaining the posterior distribution of the structural parameters.
The posterior distribution of direct estimates of M for the Patagonian scallop using Bayesian methods with an uninformative prior yielded a modal value of 0.31/y, with the 95% confidence interval in the range 0.14-0.52/y.
The posterior distribution of [sigma], shown in Figure 2, indicates that the long-run Finnish data do not give any support for the Cobb-Douglas specification.
Posterior Distributions. To simulate the posterior distribution I use a Random-Walk Metropolis-Hastings algorithmas detailed in Koop (2003).
When convinced the models have converged, we allow the sampling algorithm to continue to run to trace out the full distribution (the posterior distribution) of each hospital's values of the needp[i] and recvp[i] binomial parameters.
It is obvious from the equation of [[mu].sub.t] the sequential nature of this posterior distribution. That is, at each sampling occasion t, when more new information about concentration of S in the groundwater is received, the posterior distribution is revised forming a recursion process.
Bayesian statisticians call this distribution the posterior distribution. (4) Denoting the vector of parameters by [theta], the time series by 17, and the unobserved factor by F, let us write this distribution as p([theta],F|Y).
The second approach is the Bayesian approach that uses Gibbs sampling to improve a pre-specified prior distribution of [beta] to arrive at a posterior distribution (e.g.
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