  We introduce PPL Bench, a new benchmark for evaluating Probabilistic
Programming Languages (PPLs) on a variety of statistical models. The benchmark
includes data generation and evaluation code for a number of models as well as
implementations in some common PPLs. All of the benchmark code and PPL
implementations are available on Github. We welcome contributions of new models
and PPLs and as well as improvements in existing PPL implementations. The
purpose of the benchmark is two-fold. First, we want researchers as well as
conference reviewers to be able to evaluate improvements in PPLs in a
standardized setting. Second, we want end users to be able to pick the PPL that
is most suited for their modeling application. In particular, we are interested
in evaluating the accuracy and speed of convergence of the inferred posterior.
Each PPL only needs to provide posterior samples given a model and observation
data. The framework automatically computes and plots growth in predictive
log-likelihood on held out data in addition to reporting other common metrics
such as effective sample size and $\hat{r}$.
