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模型可视化>> Bayes-Graph-and-Causal-Inference>> 返回
项目作者: hscspring

项目描述 :
Graph based Bayes causal inference.
高级语言:

项目主页:
项目地址: git://github.com/hscspring/Bayes-Graph-and-Causal-Inference.git
创建时间: 2019-05-30T09:36:18Z
项目社区:https://github.com/hscspring/Bayes-Graph-and-Causal-Inference

开源协议:MIT License

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Bayes-Graph-and-Causal-Inference

Study

  • 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

Package

  • 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

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