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We introduce PPL Bench, a new benchmark for evaluating ProbabilisticProgramming Languages (PPLs) on a variety of statistical models. The benchmarkincludes data generation and evaluation code for a number of models as well asimplementations in some common PPLs. All of the benchmark code and PPLimplementations are available on Github. We welcome contributions of new modelsand PPLs and as well as improvements in existing PPL implementations. Thepurpose of the benchmark is two-fold. First, we want researchers as well asconference reviewers to be able to evaluate improvements in PPLs in astandardized setting. Second, we want end users to be able to pick the PPL thatis most suited for their modeling application. In particular, we are interestedin evaluating the accuracy and speed of convergence of the inferred posterior.Each PPL only needs to provide posterior samples given a model and observationdata. The framework automatically computes and plots growth in predictivelog-likelihood on held out data in addition to reporting other common metricssuch as effective sample size and $\hat{r}$.

PPL Bench: Evaluation Framework For Probabilistic Programming Languages