A Practical Sequential Stopping Rule for High-Dimensional Markov Chain Monte Carlo

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Multivariate output analysis for Markov chain Monte Carlo (2015, methodology): “Another standard way of terminating simulation is to stop when the number of effective samples for each component reaches a pre-specified lower bound (see Atkinson et al. (2008), Drummond et al. (2006), Giordano et al. (2015), Gong and Flegal (2016), and Kruschke (2014) for a few examples).”
Revisiting the Gelman–Rubin Diagnostic (2018, methodology): “e scalars. Then for m1 chains, each of length n, Vats et al. (2019) dene ESS as ESS = mn det() det(T 1) 1=p : For p= 1, this reduces to the following univariate denition of ESS as discussed by Gong and Flegal (2016) and Kass et al. (1998): ESS p=1 = mn ˙2 ˝2 1 : Strongly consistent estimators of T”