(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Aaron Haslam writes:
I have a question regarding combining the estimates from multiply imputed datasets. In the third addition of BDA on the top of page 452, you mention that with Bayesian analyses all you have to do is mix together the simulations. I want to clarify that this means you simply combine the posteriors from the MCMCs from the different datasets? For instance, with a current study I am working on I have 5 imputed datasets with missing outcome data imputed. I would generate individual posteriors for each of these datasets then mix them together to obtain a combined posterior and then calculate the summary statistics on this combined posterior.
I replied that yes, that is what I would do. But then I thought I’d post here in case anyone has other thoughts on the matter.
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