Deep universal probabilistic programming with Python and PyTorch

InferPy: Deep Probabilistic Modeling with Tensorflow Made Easy

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

Sum-product networks in Julia.

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

Distributional Gradient Boosting Machines

An extension of Py-Boost to probabilistic modelling

A normalizing flow using Bernstein polynomials for conditional density estimation.

A toolbox for inference of mixture models

Blackjack Notebook (bjnb): Probabilistic analysis and simulation

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

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

This repository introduces an adaptive formula inspired by the CHSH logic, designed to evaluate, test, and improve model performance across multiple conditions. By adapting CHSH principles into a flexible structure, it provides a systematic way to analyze results, ensure reliability, and explore deeper insights in experimentation.

Probabilistic Programming with Python and Chainer

From-scratch C++23 AI algorithms with Eigen (ML, DL, RL, evolutionary, probabilistic, graph, and causal) built with CMake/vcpkg.