Markov chain Monte Carlo convergence diagnostics

Several chains from dispersed initial states, trace plots, autocorrelation, effective sample sizes, and the rank-normalized split potential scale reduction factor $\hat{R}$ assess mixing and disagreement between chains. An $\hat{R}$ close to one is useful evidence but cannot prove convergence or prove posterior propriety. Comparing chains that explore different modes is especially important; monitor each scientifically important observable and its Monte Carlo uncertainty. A long apparent plateau can also occur when an improper-target sampler is drifting toward a boundary.

The potential scale reduction factor compares variation within simulated chains to a variance estimate also incorporating differences between chains. In its classical form, $\hat{R}=\sqrt{\hat{V}/W}$, where $W$ is the average within-chain sample variance. Values appreciably above one indicate disagreement; values close to one do not prove convergence. Splitting chains helps expose changes over time, and modern rank-based variants improve robustness.^[ref]

A trace plot displays an observable along successive iterations of a Markov chain Monte Carlo algorithm. Its horizontal axis is iteration and its vertical axis is the observed parameter or function value. Trends suggest an initial transient or slow exploration, separated regimes suggest transitions between posterior regions, and exact plateaus show repeated states. The plot should be read with acceptance statistics, autocorrelation and multiple starting states; its visual appearance alone cannot prove stationarity or identify the unique cause of poor mixing.