Courses

Probabilistic Graphical Models

• intro: Master a new way of reasoning and learning in complex domains
• instructor: Daphne Koller, Professor
• coursera: https://www.coursera.org/learn/probabilistic-graphical-models

Tutorials

Scalable learning of graphical models: A KDD’16 Tutorial

• homepage: http://www.francois-petitjean.com/Research/KDD-2016-Tutorial/
• mirror: https://pan.baidu.com/s/1kUPCeLT

Resources

A Brief Introduction to Graphical Models and Bayesian Networks

http://www.cs.ubc.ca/~murphyk/Bayes/bnintro.html

An Introduction To Graphical Models(by Michael I. Jordan. 1997)

• paper: http://www.cis.upenn.edu/~mkearns/papers/barbados/jordan-tut.pdf

Human-level concept learning through probabilistic program induction

• intro: Science 2015
• paper: http://cdn1.almosthuman.cn/wp-content/uploads/2015/12/Human-level-concept-learning-through-probabilistic-program-induction.pdf
• article: http://www.sciencemag.org/content/350/6266/1332.full
• github: https://github.com/brendenlake/BPL

Graphical Models Lectures 2015

• homepage: http://www.stats.ox.ac.uk/~lienart/gml.html

2 Graphical Models in a Nutshell

http://ai.stanford.edu/~koller/Papers/Koller+al:SRL07.pdf

An ICCV 2015 Tutorial on Inference and Learning in Discrete Graphical Models: Theory and Practice

• homepage: http://cvlab-dresden.de/research/optimization-and-learning/gm-tutorial-iccv2015/
• slides(“inference”): http://cvlab-dresden.de/wp-content/uploads/2016/01/inference.pdf
• slides(“learning”): http://cvlab-dresden.de/wp-content/uploads/2015/12/learning.pdf

Tools/Libraries

pomegranate: Fast, flexible and easy to use probabilistic modelling in Python

• github: https://github.com/jmschrei/pomegranate
• docs: http://pomegranate.readthedocs.org/en/latest/

merlin: An extensible C++ library for probabilistic inference in graphical models

• github：https://github.com/radum2275/merlin

pgmpy: Python Library for Probabilistic Graphical Models

• homepage: http://pgmpy.org/
• github: https://github.com/pgmpy/pgmpy