23 Open source projects that are alternatives of or similar to markovian

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

Differential Algebra Computational Toolbox

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions

Code for paper: MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation

Julia package for automatic Bayesian inference on a factor graph with reactive message passing

Sampling nuclear data and uncertainty

A Python Library for Deep Probabilistic Modeling

Bayesian nonparametric models for python

Blang's software development kit

Probabilistic Machine Learning course lab @Units

Software for learning sparse Bayesian networks

This project has two parts. In part one, we use markov random field to denoise an image. In Part two, we use similar model for image segmentation.

Library for graphical models of decision making, based on pgmpy and networkx

An implementation of Bayesian Networks Model for pure C++14 (11) later, including probability inference and structure learning method.

Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package

Official Repository of "Contextual Graph Markov Model" (ICML 2018 - JMLR 2020)

Separating Normalizing Flows code from Pyro and improving API

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

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.

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

LibRec: A Leading Java Library for Recommender Systems, see

Fast, flexible and easy to use probabilistic modelling in Python.