structure-learning

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

Python package 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"

Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks

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

Graph Optimiser for Learning and Evolution of Models

Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution, AAAI 2020 and NeurIPS 2019 Bayesian Deep Learning Workshop

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks". This paper is currently under review.

Sum-Product Network learning routines in python

[Experimental] Global causal discovery algorithms

DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization

Amortized Inference for Causal Structure Learning, NeurIPS 2022

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

ML4C: Seeing Causality Through Latent Vicinity

Tractable learning of Bayesian networks from partially observed data