Not yet reviewed by Pith; the record is open.

It is important to estimate the errors of probabilistic inference algorithms. Existing diagnostics for Markov chain Monte Carlo methods assume inference is asymptotically exact, and are not appropriate for approximate methods like variational inference or Laplace's method. This paper introduces a diagnostic based on repeatedly simulating datasets from the prior and performing inference on each. The central observation is that it is possible to estimate a symmetric KL-divergence defined over these simulations.