Sampling-Based Accuracy Testing of Posterior Estimators for General Inference

Understand

Parameter inference, i.e.

• inferring the posterior distribution of the parameters of a statistical model given some data, is a central problem to many scientific disciplines.
• Generative models can be used as an alternative to Markov Chain Monte Carlo methods for conducting posterior inference, both in likelihood-based and simulation-based problems.
• However, assessing the accuracy of posteriors encoded in generative models is not straightforward.