structure-learning
There are 53 repositories under structure-learning topic.
pgmpy/pgmpy
Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
erdogant/bnlearn
Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
aimclub/BAMT
Repository of a data modeling and analysis tool based on Bayesian networks
kevinsbello/dagma
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"
py-why/dodiscover
[Experimental] Global causal discovery algorithms
phlippe/ENCO
Official repository of the paper "Efficient Neural Causal Discovery without Acyclicity Constraints"
felixleopoldo/benchpress
A Snakemake workflow to run and benchmark structure learning (a.k.a. causal discovery) algorithms for probabilistic graphical models.
aimclub/GOLEM
Graph Optimiser for Learning and Evolution of Models
probsys/AutoGP.jl
Automated Bayesian model discovery for time series data
larslorch/avici
Amortized Inference for Causal Structure Learning, NeurIPS 2022
larslorch/dibs
DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021
hiarindam/document-image-classification-TL-SG
Document Image Classification with Intra-Domain Transfer Learning and Stacked Generalization of Deep Convolutional Neural Networks
agadetsky/pytorch-pl-variance-reduction
[AAAI 2020 Oral] Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution
salvaRC/Graphino
Code associated with the paper "The World as a Graph: Improving El Niño Forecasting with Graph Neural Networks".
arranger1044/spyn
Sum-Product Network learning routines in python
Howardhuang98/BNSL
Bayesian network structure learning
microsoft/ML4C
[SDM'23] ML4C: Seeing Causality Through Latent Vicinity
sfu-cl-lab/FactorBase
The source code repository for the FactorBase system
ogencoglu/causal_twitter_modeling_covid19
Source code for the paper "Causal Modeling of Twitter Activity during COVID-19". Computation, 2020.
QueensGambit/PGM-Causal-Reasoning
Experiments on structure learning of Bayesian Networks with emphasis on finding causal relationship
syanga/dglearn
Python implementation of "Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs," in ICML 2020
Duntrain/TOPO
Optimizing NOTEARS Objectives via Topological Swaps
massimo-rizzoli/BNSL-QA-python
Python implementation of Bayesian Network Structure Learning using Quantum Annealing https://doi.org/10.1140/epjst/e2015-02349-9
sergioluengosanchez/TSEM
Tractable learning of Bayesian networks from partially observed data
syanga/model-augmented-mutual-information
Code accompanying paper "Model-Augmented Conditional Mutual Information Estimation for Feature Selection" in UAI 2020
fritzbayer/Causal-Discovery-Research-Papers
A curated list of causal structure learning research papers with implementations.
HeddaCohenIndelman/PerturbedStructuredPredictorsDirect
This is the official implementation of the bipartite matching experiment from the paper "Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization".
montilab/shine
Structure Learning for Hierarchical Networks
tmadeira/ktrees
Computer Science undergraduate thesis on uniform generation of k-trees for learning the structure of Bayesian networks (USP 2016)
furrer-lab/abn
Bayesian network analysis in R
VishnuBeji/BayesianNet_QuantumAnnealing
Bayesian Network structure learning with encoding into a Quadratic Unconstrained Binary Optimisation (QUBO) problem.
ArminKmz/distributed-sparse-GGM
GGM structure learning using 1 bit.
jspieler/QBAF-Learning
Structure Learning of Gradual Bipolar Argumentation Graphs using Genetic Algorithms
python-qds/qdscreen
Quasi-determinism screening for fast Bayesian Network Structure Learning (from T.Rahier's PhD thesis, 2018)
rbalexan/aa-228
workspace for AA 228: decision making under uncertainty
noriakis/scstruc
Gene regulatory network based on Bayesian network structure in single-cell transcriptomics