Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

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

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

Sum-Product Network learning routines in python

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

The source code repository for the FactorBase system

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

Tractable learning of Bayesian networks from partially observed data

CS undergraduate thesis on uniform generation of k-trees for learning the structure of Bayesian networks (IME-USP 2016).

GGM structure learning using 1 bit.

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

Bounded Tree-width Bayesian Networks learner

Latent K-tree Bayesian Networks learner

A Bayesian network structure learning routine for collecting all networks within a factor of optimal

臺灣人工智慧學校(AIA)南部分校技術班第二期 kaggle競賽內容-森林種類預測(DNN)

Structure learning for protein signaling pathways

Learn probabilistic models with hidden variables in a k-tree structure

applying subsample ordering least angle regression to linear probabstic graph learning on high dimensional GIS-census data for house pricing

MATLAB C++ MEX code of BISN (Bayesian Inference of Sparse Networks)

Manual, TensorFlow, Spark

Published at Frontiers in Psychology - Cognition (https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02833/full)

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.