posterior probability

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Related to Posterior distribution: Prior distribution

pos·te·ri·or prob·a·bil·i·ty

the best rational assessment of the probability of an outcome on the basis of established knowledge modified and brought up to date.
See also: prior probability, Bayes theorem.
References in periodicals archive ?
They first derived a general formula for the joint posterior distribution of all parameters using the Bayes theorem.
Bayesian logistic regression model of prevalence of infection with Schistosoma haematobium in children in 418 schools in Burkina Faso, Mali, and Niger, 2004-2006 * Posterior distribution Variable Mean (95% CrI) SD Female gender 0.
We refer to the mapping from [lambda] to the marginal data density as the posterior distribution of [lambda].
To this end, we worked with the posterior distribution generated by BCal for each [A.
During preelection quarters, the posterior distribution of the interest rate smoothing coefficient PMP clearly shifts to the right (the estimated posterior mean switches from 0.
Canada), which uses a Metropolis-Hastings algorithm to sample from the joint posterior distribution of all model quantities.
To determine the relationship between ambient levels and relative risk induced by our model, we examined the joint posterior distribution of average ambient levels, [[PSI].
and application of Bayes rule yields the joint posterior distribution of the size and time of attack as
This assumption has the useful property that the posterior distribution, given any estimate x, is lognormal.
The gamma prior is the conjugate distribution (Patrick, 1972; Carlin and Louis, 2000) for the Poisson sampling distribution, which means that the posterior distribution is in the same family as the prior distribution for each stratum.
2) The Bayesian analysis proceeds by multiplying the prior distribution and the likelihood function together to obtain the posterior distribution of the model parameters, P([theta]|y).
The joint density function for the parameters given the gene expression data, denoted g([theta]|X), is referred to as the posterior distribution and can be estimated using the Markov chain Monte Carlo (MCMC) method (Hastings 1970).

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