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

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

Sum-product networks in Julia.

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

Diffusion Tutorials (Enhanced Chinese Edition) — A Chinese-translated and theory-enhanced version of tsmatz/diffusion-tutorials, bridging diffusion model theory (DDPM/SMLD/SDE) with practical PyTorch implementations.

Extended functionality for univariate probability distributions in PyTorch

Scalable probabilistic impact modeling

Train and evaluate probabilistic word embeddings with Python.

QECops is a lightweight, open-source Monte Carlo simulation framework for studying how noise assumptions influence logical error behavior in quantum error correction (QEC). The question it seeks to answer is: How sensitive are QEC performance conclusions to the choice of noise model assumptions?

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

Probabilistic ML framework for RUL prediction combining neural networks and Bayesian particle filtering for uncertainty-aware prognostics.

This project explores a novel approach to forecasting by leveraging probabilistic reasoning to model future events before they occur.