Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
structure-learning 开源项目推荐(79 个)
structure-learning 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
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按 Star 数排序展示前 20 个 · GitHub 共 79 个仓库A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"
基于贝叶斯网络的数据建模与分析工具库
[Experimental] Global causal discovery algorithms
Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"
Automated Bayesian model discovery for time series data
Scalable open-source software to run, develop, and benchmark causal discovery algorithms
Amortized Inference for Causal Structure Learning, NeurIPS 2022
Graph Optimiser for Learning and Evolution of Models
DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks".
Sum-Product Network learning routines in python
Causal Discovery in R
dagrad is a Python package that provides an extensible, modular platform for developing and experimenting with differentiable (gradient-based) structure learning methods.
Lightweight PC algorithm for causal skeleton discovery — pip + CLI + graph viz
Bayesian network structure learning
[SDM'23] ML4C: Seeing Causality Through Latent Vicinity
The source code repository for the FactorBase system
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/structure-learning。
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- 当前 20 个项目累计 1767 Star,覆盖 5 种主要语言,数据更新于 2026-11-07。
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