A community repository for benchmarking Bayesian methods
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Bayesian Benchmarks

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This is a set of tools for evaluating Bayesian models, together with benchmark implementations and results.


  • There is a lack of standardized tasks that meaningfully assess the quality of uncertainty quantification for Bayesian black-box models.
  • Variations between tasks in the literature make a direct comparison between methods difficult.
  • Implementing competing methods takes considerable effort, and there little incentive to do a good job.
  • Published papers may not always provide complete details of implementations due to space considerations.


  • Curate a set of benchmarks that meaningfully compare the efficacy of Bayesian models in real-world tasks.
  • Maintain a fair assessment of benchmark methods, with full implementations and results.


  • Classification and regression
  • Density estimation (real world and synthetic) (TODO)
  • Active learning
  • Adversarial robustness (TODO)

Current implementations:

Coming soon: