I wonder if there’s a clean way to calculate IAT from the Stan output. The documentation on LaplaceDeamon says
IATof 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 (
ESSare inversely related, though not perfectly because each is estimated a little differently. Given
NNsamples and taking autocorrelation into account,
ESSestimates a reduced number of
IATestimates 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.
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