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Approximate probabilistic inference algorithms are central to many fields.Examples include sequential Monte Carlo inference in robotics, variationalinference in machine learning, and Markov chain Monte Carlo inference instatistics. A key problem faced by practitioners is measuring the accuracy ofan approximate inference algorithm on a specific data set. This paperintroduces the auxiliary inference divergence estimator (AIDE), an algorithmfor measuring the accuracy of approximate inference algorithms. AIDE is basedon the observation that inference algorithms can be treated as probabilisticmodels and the random variables used within the inference algorithm can beviewed as auxiliary variables. This view leads to a new estimator for thesymmetric KL divergence between the approximating distributions of twoinference algorithms. The paper illustrates application of AIDE to algorithmsfor inference in regression, hidden Markov, and Dirichlet process mixturemodels. The experiments show that AIDE captures the qualitative behavior of abroad class of inference algorithms and can detect failure modes of inferencealgorithms that are missed by standard heuristics.

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms