Publication date: 05/24/2021

(Available for models fit with Hierarchal Bayes.) You can use the Bayes Chain data table to determine whether your estimates have stabilized. The table that is created has a number of rows equal to the Number of Bayesian Iterations (specified on the launch window) plus one. The first row, Iteration 1, gives the starting values. Subsequent rows show the results of the iterations, in order. The table has a column for the iteration counts, the model Log Likelihood, and columns corresponding to each model effect:

Iteration

Gives the iteration number, where the first row shows starting values.

Log Likelihood

The log-likelihood of the model for that iteration. You can plot the Log Likelihood against Iteration to view behavior over the burn-in and tuning periods.

Adaptive Sigma for <model effect>

Gives the estimate of the square root of the diagonal entries of the inverse Wishart distribution scale matrix for the corresponding effect.

Acceptance for <model effect>

Gives the sampling acceptance rate for the corresponding effect.

Mean of <model effect>

Gives the estimated mean for the corresponding effect.

Variance of <model effect>

Gives the estimated variance for the corresponding effect.

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