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Bean Machine

A universal probabilistic programming language to enable fast and accurate Bayesian analysis

Declarative modeling

Clear, intuitive syntax that lets you focus on the model and leave performance to the framework.

Programmable inference

Mix-and-match inference methods, proposers, and inference strategies to achieve maximum efficiency.

Powered by PyTorch

Leverage native GPU and autograd support and integrate seamlessly with the PyTorch ecosystem.

Status: Beta. APIs are likely to change. Functionalities are constantly being improved. Bug reports are welcome, but bandwidth is very limited for feature requests.