### Measuring Sample Quality with Stein’s Method

To carry out posterior inference on datasets of unprecedented sizes, practitioners are turning to biased MCMC procedures that trade off asymptotic exactness for computational efficiency. The reasoning is sound: a reduction in variance due to more rapid sampling can outweigh the bias introduced. However, the inexactness creates new challenges for sampler and parameter selection, since standard measures of sample quality like effective sample size do not account for asymptotic bias. To address these challenges, we introduce a new computable quality…