Bayesian network analysis in R
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Updated
Sep 19, 2024 - R
Bayesian network analysis in R
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
Repository of a data modeling and analysis tool based on Bayesian networks
[Experimental] Global causal discovery algorithms
Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
Gene regulatory network based on Bayesian network structure in single-cell transcriptomics
Optimizing NOTEARS Objectives via Topological Swaps
Graph Optimiser for Learning and Evolution of Models
Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"
The source code repository for the FactorBase system
Amortized Inference for Causal Structure Learning, NeurIPS 2022
DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021
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"
Automated Bayesian model discovery for time series data
Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.
Quasi-determinism screening for fast Bayesian Network Structure Learning (from T.Rahier's PhD thesis, 2018)
This is the official implementation of the bipartite matching experiment from the paper "Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization".
Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks".
Code for the paper "Dependence Structure Estimation via Copula"
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