A deep learning method for comparing Bayesian hierarchical models.

Bayesian model comparison (BMC) is a formal way to assess the relative merits of competing computational models. However, BMC is often intractable for hierarchical models due to their large number of parameters. To address this issue, we propose a deep learning method for performing BMC on any set of hierarchical models that can be instantiated as probabilistic programs. In addition to unlocking the ability to compare previously intractable models, our method enables re-estimation of model probabilities and fast performance validation prior to any real-data application.