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A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
| Date | Stars |
|---|---|
| 2026-07-31 | 2218 |
| 2026-08-01 | 2218 |
| 2026-08-06 | 2218 |
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<div align="center">
<a href="http://zhusuan.readthedocs.io"> <img width="250px" height="auto"
src="docs/_static/images/zhusuan-logo-v.png"></a>
</div>
---
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[](https://github.com/thu-ml/zhusuan/blob/master/LICENSE)
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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](https://www.tensorflow.org). 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:
* **Variational Inference (VI)** with programmable variational posteriors, various
objectives and advanced gradient estimators (SGVB, REINFORCE, VIMCO, etc.).
* **Importance Sampling (IS)** for learning and evaluating models, with programmable
proposals.
* **Hamiltonian Monte Carlo (HMC)** with parallel chains, and optional
automatic parameter tuning.
* **Stochastic Gradient Markov Chain Monte Carlo (SGMCMC)**:
SGLD, PSGLD, SGHMC, and SGNHT.
## Installation
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](https://www.tensorflow.org/install/).
If you are developing ZhuSuan, you may want to install in an
"editable" or "develop" mode. Please refer to the Contributing section below.
## Documentation
* [Tutorials and API docs](http://zhusuan.readthedocs.io)
* [ZhuSuan's white paper](https://arxiv.org/abs/1709.05870)
## Examples
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]"
```
* Gaussian:
[HMC](examples/toy_examples/gaussian.py)
* Toy 2D Intractable Posterior:
[SGVB](examples/toy_examples/toy2d_intractable.py)
* Bayesian Neural Networks:
[SGVB](examples/bayesian_neural_nets/bnn_vi.py),
[SGMCMC](examples/bayesian_neural_nets/bnn_sgmcmc.py)
* Variational Autoencoder (VAE):
[SGVB](examples/variational_autoencoders/vae.py),
[IWAE](examples/variational_autoencoders/iwae.py)
* Convolutional VAE:
[SGVB](examples/variational_autoencoders/vae_conv.py)
* Semi-supervised VAE (Kingma, 2014):
[SGVB](examples/semi_supervised_vae/vae_ssl.py),
[Adaptive IS](examples/semi_supervised_vae/vae_ssl_adaptive_is.py)
* Deep Sigmoid Belief Networks
[Adaptive IS](examples/sigmoid_belief_nets/sbn_adaptive_is.py),
[VIMCO](examples/sigmoid_belief_nets/sbn_vimco.py)
* Logistic Normal Topic Model:
[HMC](examples/topic_models/lntm_mcem.py)
* Probabilistic Matrix Factorization:
[HMC](examples/probabilistic_matrix_factorization/pmf_hmc.py)
* Sparse Variational Gaussian Process:
[SGVB](examples/gaussian_process/svgp.py)
## Citing ZhuSuan
If you find ZhuSuan useful, please cite it in your publications.
We provide a BibTeX entry of the ZhuSuan white paper below.
```
@ARTICLE{zhusuan2017,
title={Zhu{S}uan: A Library for {BExcerpt of 4,390 characters
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