DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

Project for the exams of FAIKR Module 3, Master in AI at UNIBO

A Python library that helps data scientists to infer causation rather than observing correlation.

Project for Probability for Computer Science - bayesian network modeling of risk in arctic navigation

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

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

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

2023/24 Früh­jahrs­se­mes­ter Statistical Models in Computational Biology @ ETHz

Extension of the NormAN base-model to study the effects of agreeableness on information exchange.

⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.

Repository of a data modeling and analysis tool based on Bayesian networks

A Python library for CStrees

The source code repository for the FactorBase system

Heart Disease Risk Assessment using Bayesian Networks

Heart Disease Risk Assessment using Bayesian Networks

Fast and Easy Infinite Neural Networks in Python

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

A novel structure learning algorithm for optimal Bayesian Networks in WEKA.

This aim of this project is to analyze globular star clusters in the Milky Way, in order to understand their dynamics. The conducted study examined the properties that affect the central velocity dispersion, their impact and the correlations between them.