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Example 26
Replicating Bayesian Analysis and Data Imputation Results
The multiple imputation and Bayesian estimation algorithms implemented in Amos
make extensive use of a stream of random numbers that depends on an initial random
number seed. The default behavior of Amos is to change the random number seed
every time you perform Bayesian estimation, Bayesian data imputation, or stochastic
regression data imputation. Consequently, when you try to replicate one of those
analyses, you can expect to get slightly different results because of using a different
random number seed.
If, for any reason, you need an exact replication of an earlier analysis, you can do
so by starting with the same random number seed that was used in the earlier analysis.
Examining the Current Seed
To find out what the current random number seed is or to change its value:
E From the menus, choose Tools > Seed Manager.
By default, Amos increments the current random number seed by one for each
invocation of a simulation method that makes use of random numbers (either Bayesian
SEM, stochastic regression data imputation, or Bayesian data imputation). Amos