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ZhuSuan is a Python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon TensorFlow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan provides deep learning style primitives and algorithms for building probabilistic models and applying Bayesian inference. The supported inference algorithms include:


ZhuSuan is still under development. Before the first stable release (1.0), please clone the repository and run

pip install .

in the main directory. This will install ZhuSuan and its dependencies automatically. ZhuSuan also requires TensorFlow 1.13.0 or later. Because users should choose whether to install the cpu or gpu version of TensorFlow, we do not include it in the dependencies. See Installing TensorFlow.

If you are developing ZhuSuan, you may want to install in an "editable" or "develop" mode. Please refer to the Contributing section below.



We provide examples on traditional hierarchical Bayesian models and recent deep generative models.

To run the provided examples, you may need extra dependencies to be installed. This can be done by

pip install ".[examples]"

Citing ZhuSuan

If you find ZhuSuan useful, please cite it in your publications. We provide a BibTeX entry of the ZhuSuan white paper below.

    title={Zhu{S}uan: A Library for {B}ayesian Deep Learning},
    author={Shi, Jiaxin and Chen, Jianfei. and Zhu, Jun and Sun, Shengyang
    and Luo, Yucen and Gu, Yihong and Zhou, Yuhao},
    journal={arXiv preprint arXiv:1709.05870},


We always welcome contributions to help make ZhuSuan better. If you would like to contribute, please check out the guidelines here.