Autocorrelations and differencing

General
1

Hi Stan community

I was wondering how one should treat posterior samples of a stanfit object regarding the autocorrelation of the samples.
Say we sample 10k samples after 10k warmup.

ESS, Rhat are okay / good.

Yet, when we look at the ACF of saying a scale parameter, ACF goes to 0 around the 15th observation for all 4 chains.

Should we then thin the series to every 15th value to have proper inferences and predictions?

Sorry for this likely very basic question… I could not find a satisfying answer with a couple of google searches…

2

I guess it would be easiest to simply re-run the model and increase thin to 15?

3

Just use all of your draws. (Unless there are memory issues, then thinning could help)

4

Yes, the reason is that you get better estimates with all the draws. The MCMC standard error being reported gives you standard errors on mean estimates.

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