Deep universal probabilistic programming with Python and PyTorch

a python framework to build, learn and reason about probabilistic circuits and tensor networks

A collection of Methods and Models for various architectures of Artificial Neural Networks

A scalable and accurate probabilistic network configuration analyzer verifying network properties in the face of random failures.

An extension of Py-Boost to probabilistic modelling

A toolbox for inference of mixture models

Repository to reproduce "Cascade-based Echo Chamber Detection" accepted at CIKM2022

Extended functionality for univariate probability distributions in PyTorch

Train and evaluate probabilistic word embeddings with Python.

Scalable probabilistic impact modeling

A classifier that distinguishes English vs. Spanish using only A–Z letter frequencies and a multinomial Bayesian model.

The interface library for probabilistic modeling in HEP

Tensor-Network Machine Learning with Matrix Product States, trained via a surrogate (projective) loss instead of standard negative log-likelihood

This project implements probabilistic machine learning methods, including Bayesian classification, Gaussian discriminant models, and dropout in neural networks. It explores softmax regression, log-likelihood optimization, and performance evaluation using accuracy, ROC curves, and confusion matrices.