VWAMonoGGM

Bayesian Modeling and Probabilistic Programming in Python

Deep universal probabilistic programming with Python and PyTorch

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.

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

A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

Gaussian processes in TensorFlow

Awesome resources on normalizing flows.

Seminars DeepBayes Summer School 2018

[NeurIPS 2022] Denoising Diffusion Restoration Models -- Official Code Repository

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.