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Example 26
You can change the refresh interval to something other than the default of 1,000
observations. Alternatively, you can refresh the display at a regular time interval that
you specify.
If you select Refresh the display manually, the display will never be updated
automatically. Regardless of what you select on the
Refresh tab, you can refresh the
display manually at any time by clicking the Refresh button on the Bayesian SEM
toolbar.
Assessing Convergence
Are there enough samples to yield stable estimates of the parameters? Before
addressing this question, let us briefly discuss what it means for the procedure to have
converged. Convergence of an MCMC algorithm is quite different from convergence
of a nonrandom method such as maximum likelihood. To properly understand MCMC
convergence, we need to distinguish two different types.
The first type, which we may call convergence in distribution, means that the
analysis samples are, in fact, being drawn from the actual joint posterior distribution
of the parameters. Convergence in distribution takes place in the burn-in period, during
which the algorithm gradually forgets its initial starting values. Because these samples
may not be representative of the actual posterior distribution, they are discarded. The
default burn-in period of 500 is quite conservative, much longer than needed for most
problems. Once the burn-in period is over and Amos begins to collect the analysis