Graphical Models Resources
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)
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
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
merlin: An extensible C++ library for probabilistic inference in graphical models
pgmpy: Python Library for Probabilistic Graphical Models
- homepage: http://pgmpy.org/
- github: https://github.com/pgmpy/pgmpy
