  The generality and robustness of inference algorithms is critical to the
success of widely used probabilistic programming languages such as Stan, PyMC,
Pyro, and Turing.jl. When designing a new general-purpose inference algorithm,
whether it involves Monte Carlo sampling or variational approximation, the
fundamental problem arises in evaluating its accuracy and efficiency across a
range of representative target models. To solve this problem, we propose
posteriordb, a database of models and data sets defining target densities along
with reference Monte Carlo draws. We further provide a guide to the best
practices in using posteriordb for model evaluation and comparison. To provide
a wide range of realistic target densities, posteriordb currently comprises 120
representative models and has been instrumental in developing several general
inference algorithms.
