- bayesgroup/deepbayes-2018: Seminars DeepBayes Summer School 2018
- CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
- MIT Computational Cognitive Science Group - Resources
- Directed GMs: Bayesian Networks
- A Tutorial on Inference and Learning in Bayesian Networks
- Bayesian networks
- 10708 Probabilistic Graphical Models
- Causal Inference Book | Miguel Hernan | Harvard T.H. Chan School of Public Health
- jmschrei/pomegranate: Fast, flexible and easy to use probabilistic modelling in Python.
- deepmind/graph_nets: Build Graph Nets in Tensorflow
- thu-ml/zhusuan: A Library for Bayesian Deep Learning, Generative Models, Based on Tensorflow
- AI-DI/Brancher: A user-centered Python package for differentiable probabilistic inference
- microsoft/dowhy: DoWhy is a Python library that makes it easy to estimate causal effects. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
- pytorch/botorch: Bayesian optimization in PyTorch