BayesComp
This python library is an extension of pytorch 
for transforming ordinary neural networks into Bayesian. Why? Check out our post for motivation and approaches in this topic. You will find here foundamental ideas and practical considerations about Bayesian inference.
Authors
- Ilgam Latypov
- Alexander Terentyev
- Kirill Semkin
- Nikita Mashalov
References
Here are the works upon which this library is built
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Graves, A. (2011). Practical Variational Inference for Neural Networks. In Advances in Neural Information Processing Systems. Curran Associates, Inc.
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Christos Louizos, Karen Ullrich, & Max Welling. (2017). Bayesian Compression for Deep Learning.
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Hippolyt Ritter, Aleksandar Botev, & David Barber (2018). A Scalable Laplace Approximation for Neural Networks. In International Conference on Learning Representations.
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Yingzhen Li, & Richard E. Turner. (2016). Renyi Divergence Variational Inference.