graphical-models

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 Toolkit for Causal and Probabilistic Reasoning

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

DGMs for NLP. A roadmap.

Auton Survival - an open source package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Events

Sum Product Flow: An Easy and Extensible Library for Sum-Product Networks

Scikit-learn compatible estimation of general graphical models

Graphical language server platform for building web-based diagram editors

Robopy is a python port for Robotics Toolbox in Matlab created by Peter Corke

Scalable inference for a generative model of astronomical images

Overview and implementation of Belief Propagation and Loopy Belief Propagation algorithms: sum-product, max-product, max-sum

Graphical modeling and code generation tool based on Hierarchical State Machines (UML Statecharts) and QP Real-Time Event Frameworks

Input Output Hidden Markov Model (IOHMM) in Python

Factored inference for discrete-continuous smoothing and mapping.

pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

Kalman Variational Auto-Encoder

Deep Markov Models

A toolbox for differentially private data generation

A Java Toolbox for Scalable Probabilistic Machine Learning