I wonder if there’s a clean way to calculate IAT from the Stan output. The documentation on LaplaceDeamon says

The

`IAT`

of a continuous chain correlates with the variability of the mean of the chain and relates to Effective Sample Size (`ESS`

) and Monte Carlo Standard Error (`MCSE`

).`IAT`

and`ESS`

are inversely related, though not perfectly because each is estimated a little differently. Given`NN`

samples and taking autocorrelation into account,`ESS`

estimates a reduced number of`MM`

samples. Conversely,`IAT`

estimates the number of autocorrelated samples, on average, required to produce one independently drawn sample.

I came across the Sokal (1997) reference, but could not see how to relate his definition of IAT with the ESS/MCSE measures available in posterior::summary(). I understand that this is something that is only meaningful per chain.

References:

Sokal, A. (1997). *Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms. NATO ASI Series, 131–192.* doi:10.1007/978-1-4899-0319-8_6