posterior distribution


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Related to posterior distribution: Prior distribution

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.
References in periodicals archive ?
Taking into account the posterior distribution, the posterior mean probability of each outcome is given by [mathematical expression not reproducible].
The above formulation involves two approximations which are using the approximate posterior distribution [Q.
Although our model is straightforward, the fitting and computation of posteriors cannot be feasibly performed deterministically, and so the posterior distribution was sampled using the Markov Chain Monte Carlo (MCMC) algorithm (large left arrow, Fig.
habitat complexity) were obtained from the posterior distribution simulated by sim.
He pointed out that a figure in the online appendix shows the prior and posterior distributions of these parameters, and documents that for important parameters--namely, the standard deviations or trends to the convenience yield--the posterior is quite different from the prior, suggesting that the data are informative.
Since the marginal posterior distributions of the parameters, [omega], [alpha], [beta] are skewed (see Figure 3) the median is used as an estimate.
So we iterate each chain 2 million times and use the later 1 million iterations to analyze the posterior distributions of parameters and unknown quantities of interest.
The subjective beliefs are updated by adding new information to the sampling distribution through Bayes' theorem obtaining a posterior distribution, which is used to make inferences on the parameters of the sampling model.
In Bayesian hypothesis testing [3] [6], the posterior distribution obtained by model calibration is used as the null hypothesis and an alternative distribution is selected for the alternative hypothesis.
A prior probability distribution is said to be conjugate to the sampling density if the resulting posterior distribution is a member of the same parametric family as the prior.
And their posterior distribution is computed by accurate inferring after other variables are instantiated.
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