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Bayesian Estimation
The Bayesian SEM window appears, and the MCMC algorithm immediately begins
generating samples.
The Bayesian SEM window has a toolbar near the top of the window and has a results
summary table below. Each row of the summary table describes the marginal posterior
distribution of a single model parameter. The first column, labeled Mean, contains the
posterior mean, which is the center or average of the posterior distribution. This can be
used as a Bayesian point estimate of the parameter, based on the data and the prior
distribution. With a large dataset, the posterior mean will tend to be close to the
maximum likelihood estimate. (In this case, the two are somewhat close; compare the
posterior mean of –6.536 for the age-vocabulary covariance to the maximum
likelihood estimate of –5.014 reported earlier.)