Specifying burn in for Bayes PreviousNext
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 Trang Q. Nguyen posted on Monday, December 08, 2014 - 9:24 pm
I understand the default in Mplus is to discard half of the iterations as burn-in. Could I specify something different, a specific number of iterations or a different ratio (e.g., 1/10)? I am working on a model that converges very fast, but some of the parameters have large auto-correlation, so I would like to use a big thinning factor and run long chains. This means the burn-in I need is only a small fraction of the number of actual iterations. Thanks!
 Tihomir Asparouhov posted on Tuesday, December 09, 2014 - 10:36 am
Auto correlations are not a reason of concern in principle. I would recommend to not alter Mplus convergence decision and posterior distribution. If the auto correlations is extremely high the MCMC sequence can not possibly converge fast. I would recommend using thin as you have done but leave the rest as is.

You can save all parameters in all MCMC iterations with the BPARAMETERS and summarize the posteriors any way you want from that file.
 Trang Q. Nguyen posted on Wednesday, December 10, 2014 - 12:18 pm
Thank you, Tihomir.
 Trang Q. Nguyen posted on Wednesday, December 10, 2014 - 5:03 pm
Hi Tihomir,

I just wanted to share a bit more. I used four chains and set BCONVERGENCE = 0.001, and convergence happens within a few hundred iterations. This is very clear from the trace plots, where the four chains quickly converge and mixing is very good. Most of the parameters have low auto-correlation, except for binary variables' thresholds and regression coefficients relating these to a latent mediator. Auto-correlation is not a concern except that it reduces effective sample size, and I was trying to get to effective sample sizes that I feel comfortable with (a few thousand).

Yes, I have saved all the iterations and discarded only part of the first half. I was just wondering whether there was a way to specify a burn-in option so you don't have to do this manually.

Thank you.
 Trang Q. Nguyen posted on Wednesday, December 10, 2014 - 5:16 pm
PS: Sorry, I misspoke. In that model I have an observed continuous mediator, which is why it converges very fast. I have another model with a latent mediator underlying an ordinal variable; that one takes a longer time to converge.
 Tihomir Asparouhov posted on Monday, December 15, 2014 - 9:50 am
Trang

Use FBITER command to specify whatever number of iterations you want.

BCONVERGENCE = 0.001 is a very strict criterion and I have no doubt the model has converged.

Models with categorical variables in general tend to take longer to converge and have higher auto correlations.

Tihomir
 Jonathan L. Helm posted on Tuesday, April 26, 2016 - 4:31 pm
If the BCONVERGENCE criterion is set to .001, what is the PSR boundary for convergence?

I know that the PSR boundary is based on the number of parameters estimated within the model. Within this note:
http://www.statmodel.com/download/Bayes2.pdf

on page 8, some text indicates that convergence is reached when PSR values for all parameters is less than 1 + e, where e = f*c. c is set by the user (via BCONVERGENCE = c), but what is f? How can we determine f?

Thanks!
 Tihomir Asparouhov posted on Wednesday, April 27, 2016 - 11:34 am
You should use FBITER if the automatic setup is not working for your models. It should work for most standard models.

In excel
f=NORMINV(0.95^(1/p),0,1)/1.64485362695147
where p is the number of model parameters.
Thus if p=1 then f=1.
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