Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

Repository of a data modeling and analysis tool based on Bayesian networks

Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"

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"

A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.

[Experimental] Global causal discovery algorithms

Graph Optimiser for Learning and Evolution of Models

Automated Bayesian model discovery for time series data

Amortized Inference for Causal Structure Learning, NeurIPS 2022

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

Bayesian network structure learning

Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.

Experiments on structure learning of Bayesian Networks with emphasis on finding causal relationship

The source code repository for the FactorBase system

[SDM'23] ML4C: Seeing Causality Through Latent Vicinity