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这是一个 Pyro 教程的中文编译文件。

如果教程编译进展顺利，可以制作更多的教程，理解源代码。

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Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind:

• Universal: Pyro is a universal PPL - it can represent any computable probability distribution.
• Scalable: Pyro scales to large data sets with little overhead compared to hand-written code.
• Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions.
• Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference.

Citation

If you use Pyro, please consider citing:

@article{bingham2018pyro,
  author = {Bingham, Eli and Chen, Jonathan P. and Jankowiak, Martin and Obermeyer, Fritz and
            Pradhan, Neeraj and Karaletsos, Theofanis and Singh, Rohit and Szerlip, Paul and
            Horsfall, Paul and Goodman, Noah D.},
  title = {{Pyro: Deep Universal Probabilistic Programming}},
  journal = {arXiv preprint arXiv:1810.09538},
  year = {2018}
}