Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
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Updated
Jul 15, 2024 - Python
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.
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.
Repository of a data modeling and analysis tool based on Bayesian networks
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
[Experimental] Global causal discovery algorithms
Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"
Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks".
DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021
The source code repository for the FactorBase system
Graph Optimiser for Learning and Evolution of Models
Automated Bayesian model discovery for time series data
Amortized Inference for Causal Structure Learning, NeurIPS 2022
Sum-Product Network learning routines in python
This R-package is for learning the structure of the type of graphical models called t-cherry trees from data. The structure is determined either directly from data or by increasing the order of a lower order t-cherry tree.
Optimizing NOTEARS Objectives via Topological Swaps
Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.
A Bayesian network structure learning routine for collecting all networks within a factor of optimal
Structure Learning for Hierarchical Networks
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