https://github.com/hscspring/bayes-graph-and-causal-inference

Graph based Bayes causal inference.
https://github.com/hscspring/bayes-graph-and-causal-inference

bayesian-inference bayesian-network causal-inference

Last synced: 7 days ago
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Graph based Bayes causal inference.

• Host: GitHub
• URL: https://github.com/hscspring/bayes-graph-and-causal-inference
• Owner: hscspring
• License: mit
• Created: 2019-05-30T09:36:18.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-02T07:47:16.000Z (over 5 years ago)
• Last Synced: 2024-10-18T00:48:43.165Z (26 days ago)
• Topics: bayesian-inference, bayesian-network, causal-inference
• Homepage:
• Size: 4.88 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Bayes-Graph-and-Causal-Inference

## Study

- [bayesgroup/deepbayes-2018: Seminars DeepBayes Summer School 2018](https://github.com/bayesgroup/deepbayes-2018)

- [CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)

- [MIT Computational Cognitive Science Group - Resources](http://cocosci.mit.edu/resources)

- [Directed GMs: Bayesian Networks](http://www.cs.cmu.edu/~epxing/Class/10708/lectures/lecture2-BNrepresentation.pdf)

- [A Tutorial on Inference and Learning in Bayesian Networks](http://www.ee.columbia.edu/~vittorio/Lecture12.pdf)

- [Bayesian networks](https://courses.cs.washington.edu/courses/cse515/09sp/slides/bnets.pdf)

- [10708 Probabilistic Graphical Models](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html)

- [Causal Inference Book | Miguel Hernan | Harvard T.H. Chan School of Public Health](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/)

## Package

- [jmschrei/pomegranate: Fast, flexible and easy to use probabilistic modelling in Python.](https://github.com/jmschrei/pomegranate)

- [deepmind/graph_nets: Build Graph Nets in Tensorflow](https://github.com/deepmind/graph_nets)

- [thu-ml/zhusuan: A Library for Bayesian Deep Learning, Generative Models, Based on Tensorflow](https://github.com/thu-ml/zhusuan)

- [AI-DI/Brancher: A user-centered Python package for differentiable probabilistic inference](https://github.com/AI-DI/Brancher)

- [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.](https://github.com/Microsoft/dowhy)

- [pytorch/botorch: Bayesian optimization in PyTorch](https://github.com/pytorch/botorch)