I want to use PROC MCMC to run a random-effects linear negative binomial (NB1) model and estimate ~200,000 random intercepts (as an alternative to NLMIXED). Since the OUTPOST= dataset can only save up to 32,767 variables, what approaches can I take to be able to output the posterior samples for all of the ~200K random-effects parameters?
An easy way is to assume the posterior on each effect is Gaussian:
1) Monitor the posterior distributions of the random effects.
2) Write ODS output postsumint=Px;
3) In a subsequent datastep, sample from the mean and Standard deviation given in Px (or just use the posterior info in Px.)
Let me know if this isn't what you are thinking...
G
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