Graphical Models 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 (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

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/